statistical and research

Effective Use of Statistics in Research – Methods and Tools for Data Analysis

Remember that impending feeling you get when you are asked to analyze your data! Now that you have all the required raw data, you need to statistically prove your hypothesis. Representing your numerical data as part of statistics in research will also help in breaking the stereotype of being a biology student who can’t do math.

Statistical methods are essential for scientific research. In fact, statistical methods dominate the scientific research as they include planning, designing, collecting data, analyzing, drawing meaningful interpretation and reporting of research findings. Furthermore, the results acquired from research project are meaningless raw data unless analyzed with statistical tools. Therefore, determining statistics in research is of utmost necessity to justify research findings. In this article, we will discuss how using statistical methods for biology could help draw meaningful conclusion to analyze biological studies.

Table of Contents

Role of Statistics in Biological Research

Statistics is a branch of science that deals with collection, organization and analysis of data from the sample to the whole population. Moreover, it aids in designing a study more meticulously and also give a logical reasoning in concluding the hypothesis. Furthermore, biology study focuses on study of living organisms and their complex living pathways, which are very dynamic and cannot be explained with logical reasoning. However, statistics is more complex a field of study that defines and explains study patterns based on the sample sizes used. To be precise, statistics provides a trend in the conducted study.

Biological researchers often disregard the use of statistics in their research planning, and mainly use statistical tools at the end of their experiment. Therefore, giving rise to a complicated set of results which are not easily analyzed from statistical tools in research. Statistics in research can help a researcher approach the study in a stepwise manner, wherein the statistical analysis in research follows –

1. Establishing a Sample Size

Usually, a biological experiment starts with choosing samples and selecting the right number of repetitive experiments. Statistics in research deals with basics in statistics that provides statistical randomness and law of using large samples. Statistics teaches how choosing a sample size from a random large pool of sample helps extrapolate statistical findings and reduce experimental bias and errors.

2. Testing of Hypothesis

When conducting a statistical study with large sample pool, biological researchers must make sure that a conclusion is statistically significant. To achieve this, a researcher must create a hypothesis before examining the distribution of data. Furthermore, statistics in research helps interpret the data clustered near the mean of distributed data or spread across the distribution. These trends help analyze the sample and signify the hypothesis.

3. Data Interpretation Through Analysis

When dealing with large data, statistics in research assist in data analysis. This helps researchers to draw an effective conclusion from their experiment and observations. Concluding the study manually or from visual observation may give erroneous results; therefore, thorough statistical analysis will take into consideration all the other statistical measures and variance in the sample to provide a detailed interpretation of the data. Therefore, researchers produce a detailed and important data to support the conclusion.

Types of Statistical Research Methods That Aid in Data Analysis

Statistical analysis is the process of analyzing samples of data into patterns or trends that help researchers anticipate situations and make appropriate research conclusions. Based on the type of data, statistical analyses are of the following type:

1. Descriptive Analysis

The descriptive statistical analysis allows organizing and summarizing the large data into graphs and tables . Descriptive analysis involves various processes such as tabulation, measure of central tendency, measure of dispersion or variance, skewness measurements etc.

2. Inferential Analysis

The inferential statistical analysis allows to extrapolate the data acquired from a small sample size to the complete population. This analysis helps draw conclusions and make decisions about the whole population on the basis of sample data. It is a highly recommended statistical method for research projects that work with smaller sample size and meaning to extrapolate conclusion for large population.

3. Predictive Analysis

Predictive analysis is used to make a prediction of future events. This analysis is approached by marketing companies, insurance organizations, online service providers, data-driven marketing, and financial corporations.

4. Prescriptive Analysis

Prescriptive analysis examines data to find out what can be done next. It is widely used in business analysis for finding out the best possible outcome for a situation. It is nearly related to descriptive and predictive analysis. However, prescriptive analysis deals with giving appropriate suggestions among the available preferences.

5. Exploratory Data Analysis

EDA is generally the first step of the data analysis process that is conducted before performing any other statistical analysis technique. It completely focuses on analyzing patterns in the data to recognize potential relationships. EDA is used to discover unknown associations within data, inspect missing data from collected data and obtain maximum insights.

6. Causal Analysis

Causal analysis assists in understanding and determining the reasons behind “why” things happen in a certain way, as they appear. This analysis helps identify root cause of failures or simply find the basic reason why something could happen. For example, causal analysis is used to understand what will happen to the provided variable if another variable changes.

7. Mechanistic Analysis

This is a least common type of statistical analysis. The mechanistic analysis is used in the process of big data analytics and biological science. It uses the concept of understanding individual changes in variables that cause changes in other variables correspondingly while excluding external influences.

Important Statistical Tools In Research

Researchers in the biological field find statistical analysis in research as the scariest aspect of completing research. However, statistical tools in research can help researchers understand what to do with data and how to interpret the results, making this process as easy as possible.

1. Statistical Package for Social Science (SPSS)

It is a widely used software package for human behavior research. SPSS can compile descriptive statistics, as well as graphical depictions of result. Moreover, it includes the option to create scripts that automate analysis or carry out more advanced statistical processing.

2. R Foundation for Statistical Computing

This software package is used among human behavior research and other fields. R is a powerful tool and has a steep learning curve. However, it requires a certain level of coding. Furthermore, it comes with an active community that is engaged in building and enhancing the software and the associated plugins.

3. MATLAB (The Mathworks)

It is an analytical platform and a programming language. Researchers and engineers use this software and create their own code and help answer their research question. While MatLab can be a difficult tool to use for novices, it offers flexibility in terms of what the researcher needs.

4. Microsoft Excel

Not the best solution for statistical analysis in research, but MS Excel offers wide variety of tools for data visualization and simple statistics. It is easy to generate summary and customizable graphs and figures. MS Excel is the most accessible option for those wanting to start with statistics.

5. Statistical Analysis Software (SAS)

It is a statistical platform used in business, healthcare, and human behavior research alike. It can carry out advanced analyzes and produce publication-worthy figures, tables and charts .

6. GraphPad Prism

It is a premium software that is primarily used among biology researchers. But, it offers a range of variety to be used in various other fields. Similar to SPSS, GraphPad gives scripting option to automate analyses to carry out complex statistical calculations.

This software offers basic as well as advanced statistical tools for data analysis. However, similar to GraphPad and SPSS, minitab needs command over coding and can offer automated analyses.

Use of Statistical Tools In Research and Data Analysis

Statistical tools manage the large data. Many biological studies use large data to analyze the trends and patterns in studies. Therefore, using statistical tools becomes essential, as they manage the large data sets, making data processing more convenient.

Following these steps will help biological researchers to showcase the statistics in research in detail, and develop accurate hypothesis and use correct tools for it.

There are a range of statistical tools in research which can help researchers manage their research data and improve the outcome of their research by better interpretation of data. You could use statistics in research by understanding the research question, knowledge of statistics and your personal experience in coding.

Have you faced challenges while using statistics in research? How did you manage it? Did you use any of the statistical tools to help you with your research data? Do write to us or comment below!

Frequently Asked Questions

Statistics in research can help a researcher approach the study in a stepwise manner: 1. Establishing a sample size 2. Testing of hypothesis 3. Data interpretation through analysis

Statistical methods are essential for scientific research. In fact, statistical methods dominate the scientific research as they include planning, designing, collecting data, analyzing, drawing meaningful interpretation and reporting of research findings. Furthermore, the results acquired from research project are meaningless raw data unless analyzed with statistical tools. Therefore, determining statistics in research is of utmost necessity to justify research findings.

Statistical tools in research can help researchers understand what to do with data and how to interpret the results, making this process as easy as possible. They can manage large data sets, making data processing more convenient. A great number of tools are available to carry out statistical analysis of data like SPSS, SAS (Statistical Analysis Software), and Minitab.

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Holistic but delineating. A very good read.

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• Choosing the Right Statistical Test | Types & Examples

Choosing the Right Statistical Test | Types & Examples

Published on January 28, 2020 by Rebecca Bevans . Revised on June 22, 2023.

Statistical tests are used in hypothesis testing . They can be used to:

• determine whether a predictor variable has a statistically significant relationship with an outcome variable.
• estimate the difference between two or more groups.

Statistical tests assume a null hypothesis of no relationship or no difference between groups. Then they determine whether the observed data fall outside of the range of values predicted by the null hypothesis.

If you already know what types of variables you’re dealing with, you can use the flowchart to choose the right statistical test for your data.

Statistical tests flowchart

Table of contents

What does a statistical test do, when to perform a statistical test, choosing a parametric test: regression, comparison, or correlation, choosing a nonparametric test, flowchart: choosing a statistical test, other interesting articles, frequently asked questions about statistical tests.

Statistical tests work by calculating a test statistic – a number that describes how much the relationship between variables in your test differs from the null hypothesis of no relationship.

It then calculates a p value (probability value). The p -value estimates how likely it is that you would see the difference described by the test statistic if the null hypothesis of no relationship were true.

If the value of the test statistic is more extreme than the statistic calculated from the null hypothesis, then you can infer a statistically significant relationship between the predictor and outcome variables.

If the value of the test statistic is less extreme than the one calculated from the null hypothesis, then you can infer no statistically significant relationship between the predictor and outcome variables.

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You can perform statistical tests on data that have been collected in a statistically valid manner – either through an experiment , or through observations made using probability sampling methods .

For a statistical test to be valid , your sample size needs to be large enough to approximate the true distribution of the population being studied.

To determine which statistical test to use, you need to know:

• whether your data meets certain assumptions.
• the types of variables that you’re dealing with.

Statistical assumptions

Statistical tests make some common assumptions about the data they are testing:

• Independence of observations (a.k.a. no autocorrelation): The observations/variables you include in your test are not related (for example, multiple measurements of a single test subject are not independent, while measurements of multiple different test subjects are independent).
• Homogeneity of variance : the variance within each group being compared is similar among all groups. If one group has much more variation than others, it will limit the test’s effectiveness.
• Normality of data : the data follows a normal distribution (a.k.a. a bell curve). This assumption applies only to quantitative data .

If your data do not meet the assumptions of normality or homogeneity of variance, you may be able to perform a nonparametric statistical test , which allows you to make comparisons without any assumptions about the data distribution.

If your data do not meet the assumption of independence of observations, you may be able to use a test that accounts for structure in your data (repeated-measures tests or tests that include blocking variables).

Types of variables

The types of variables you have usually determine what type of statistical test you can use.

Quantitative variables represent amounts of things (e.g. the number of trees in a forest). Types of quantitative variables include:

• Continuous (aka ratio variables): represent measures and can usually be divided into units smaller than one (e.g. 0.75 grams).
• Discrete (aka integer variables): represent counts and usually can’t be divided into units smaller than one (e.g. 1 tree).

Categorical variables represent groupings of things (e.g. the different tree species in a forest). Types of categorical variables include:

• Ordinal : represent data with an order (e.g. rankings).
• Nominal : represent group names (e.g. brands or species names).
• Binary : represent data with a yes/no or 1/0 outcome (e.g. win or lose).

Choose the test that fits the types of predictor and outcome variables you have collected (if you are doing an experiment , these are the independent and dependent variables ). Consult the tables below to see which test best matches your variables.

Parametric tests usually have stricter requirements than nonparametric tests, and are able to make stronger inferences from the data. They can only be conducted with data that adheres to the common assumptions of statistical tests.

The most common types of parametric test include regression tests, comparison tests, and correlation tests.

Regression tests

Regression tests look for cause-and-effect relationships . They can be used to estimate the effect of one or more continuous variables on another variable.

Comparison tests

Comparison tests look for differences among group means . They can be used to test the effect of a categorical variable on the mean value of some other characteristic.

T-tests are used when comparing the means of precisely two groups (e.g., the average heights of men and women). ANOVA and MANOVA tests are used when comparing the means of more than two groups (e.g., the average heights of children, teenagers, and adults).

Correlation tests

Correlation tests check whether variables are related without hypothesizing a cause-and-effect relationship.

These can be used to test whether two variables you want to use in (for example) a multiple regression test are autocorrelated.

Non-parametric tests don’t make as many assumptions about the data, and are useful when one or more of the common statistical assumptions are violated. However, the inferences they make aren’t as strong as with parametric tests.

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This flowchart helps you choose among parametric tests. For nonparametric alternatives, check the table above.

If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

• Normal distribution
• Descriptive statistics
• Measures of central tendency
• Correlation coefficient
• Null hypothesis

Methodology

• Cluster sampling
• Stratified sampling
• Types of interviews
• Cohort study
• Thematic analysis

Research bias

• Implicit bias
• Cognitive bias
• Survivorship bias
• Availability heuristic
• Nonresponse bias
• Regression to the mean

Statistical tests commonly assume that:

• the data are normally distributed
• the groups that are being compared have similar variance
• the data are independent

If your data does not meet these assumptions you might still be able to use a nonparametric statistical test , which have fewer requirements but also make weaker inferences.

A test statistic is a number calculated by a  statistical test . It describes how far your observed data is from the  null hypothesis  of no relationship between  variables or no difference among sample groups.

The test statistic tells you how different two or more groups are from the overall population mean , or how different a linear slope is from the slope predicted by a null hypothesis . Different test statistics are used in different statistical tests.

Statistical significance is a term used by researchers to state that it is unlikely their observations could have occurred under the null hypothesis of a statistical test . Significance is usually denoted by a p -value , or probability value.

Statistical significance is arbitrary – it depends on the threshold, or alpha value, chosen by the researcher. The most common threshold is p < 0.05, which means that the data is likely to occur less than 5% of the time under the null hypothesis .

When the p -value falls below the chosen alpha value, then we say the result of the test is statistically significant.

Quantitative variables are any variables where the data represent amounts (e.g. height, weight, or age).

Categorical variables are any variables where the data represent groups. This includes rankings (e.g. finishing places in a race), classifications (e.g. brands of cereal), and binary outcomes (e.g. coin flips).

You need to know what type of variables you are working with to choose the right statistical test for your data and interpret your results .

Discrete and continuous variables are two types of quantitative variables :

• Discrete variables represent counts (e.g. the number of objects in a collection).
• Continuous variables represent measurable amounts (e.g. water volume or weight).

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Statistical Analysis

Look around you. statistics are everywhere..

The field of statistics touches our lives in many ways. From the daily routines in our homes to the business of making the greatest cities run, the effects of statistics are everywhere.

Statistical Analysis Defined

What is statistical analysis? It’s the science of collecting, exploring and presenting large amounts of data to discover underlying patterns and trends. Statistics are applied every day – in research, industry and government – to become more scientific about decisions that need to be made. For example:

• Manufacturers use statistics to weave quality into beautiful fabrics, to bring lift to the airline industry and to help guitarists make beautiful music.
• Researchers keep children healthy by using statistics to analyze data from the production of viral vaccines, which ensures consistency and safety.
• Communication companies use statistics to optimize network resources, improve service and reduce customer churn by gaining greater insight into subscriber requirements.
• Government agencies around the world rely on statistics for a clear understanding of their countries, their businesses and their people.

Look around you. From the tube of toothpaste in your bathroom to the planes flying overhead, you see hundreds of products and processes every day that have been improved through the use of statistics.

Analytics Insights

Connect with the latest insights on analytics through related articles and research., more on statistical analysis.

• What are the next big trends in statistics?
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Statistics is so unique because it can go from health outcomes research to marketing analysis to the longevity of a light bulb. It’s a fun field because you really can do so many different things with it.

Besa Smith President and Senior Scientist Analydata

Statistical Computing

Traditional methods for statistical analysis – from sampling data to interpreting results – have been used by scientists for thousands of years. But today’s data volumes make statistics ever more valuable and powerful. Affordable storage, powerful computers and advanced algorithms have all led to an increased use of computational statistics.

Whether you are working with large data volumes or running multiple permutations of your calculations, statistical computing has become essential for today’s statistician. Popular statistical computing practices include:

• Statistical programming – From traditional analysis of variance and linear regression to exact methods and statistical visualization techniques, statistical programming is essential for making data-based decisions in every field.
• Econometrics – Modeling, forecasting and simulating business processes for improved strategic and tactical planning. This method applies statistics to economics to forecast future trends.
• Operations research – Identify the actions that will produce the best results – based on many possible options and outcomes. Scheduling, simulation, and related modeling processes are used to optimize business processes and management challenges.
• Matrix programming – Powerful computer techniques for implementing your own statistical methods and exploratory data analysis using row operation algorithms.
• Statistical quality improvement – A mathematical approach to reviewing the quality and safety characteristics for all aspects of production.

Careers in Statistical Analysis

With everyone from The New York Times to Google’s Chief Economist Hal Varien proclaiming statistics to be the latest hot career field, who are we to argue? But why is there so much talk about careers in statistical analysis and data science? It could be the shortage of trained analytical thinkers. Or it could be the demand for managing the latest big data strains. Or, maybe it’s the excitement of applying mathematical concepts to make a difference in the world.

If you talk to statisticians about what first interested them in statistical analysis, you’ll hear a lot of stories about collecting baseball cards as a child. Or applying statistics to win more games of Axis and Allies. It is often these early passions that lead statisticians into the field. As adults, those passions can carry over into the workforce as a love of analysis and reasoning, where their passions are applied to everything from the influence of friends on purchase decisions to the study of endangered species around the world.

Learn more about current and historical statisticians:

• Ask a statistician videos cover current uses and future trends in statistics.
• SAS loves stats profiles statisticians working at SAS.
• Celebrating statisticians commemorates statistics practitioners from history.

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Join our statistics procedures community, where you can ask questions and share your experiences with SAS statistical products. SAS Statistical Procedures

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What Is Statistical Analysis?

Statistical analysis is a technique we use to find patterns in data and make inferences about those patterns to describe variability in the results of a data set or an experiment. 

In its simplest form, statistical analysis answers questions about:

• Quantification — how big/small/tall/wide is it?
• Variability — growth, increase, decline
• The confidence level of these variabilities

What Are the 2 Types of Statistical Analysis?

• Descriptive Statistics:  Descriptive statistical analysis describes the quality of the data by summarizing large data sets into single measures. 
• Inferential Statistics:  Inferential statistical analysis allows you to draw conclusions from your sample data set and make predictions about a population using statistical tests.

What’s the Purpose of Statistical Analysis?

Using statistical analysis, you can determine trends in the data by calculating your data set’s mean or median. You can also analyze the variation between different data points from the mean to get the standard deviation . Furthermore, to test the validity of your statistical analysis conclusions, you can use hypothesis testing techniques, like P-value, to determine the likelihood that the observed variability could have occurred by chance.

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Statistical Analysis Methods

There are two major types of statistical data analysis: descriptive and inferential. 

Descriptive Statistical Analysis

Descriptive statistical analysis describes the quality of the data by summarizing large data sets into single measures. 

Within the descriptive analysis branch, there are two main types: measures of central tendency (i.e. mean, median and mode) and measures of dispersion or variation (i.e. variance , standard deviation and range). 

For example, you can calculate the average exam results in a class using central tendency or, in particular, the mean. In that case, you’d sum all student results and divide by the number of tests. You can also calculate the data set’s spread by calculating the variance. To calculate the variance, subtract each exam result in the data set from the mean, square the answer, add everything together and divide by the number of tests.

Inferential Statistics

On the other hand, inferential statistical analysis allows you to draw conclusions from your sample data set and make predictions about a population using statistical tests. 

There are two main types of inferential statistical analysis: hypothesis testing and regression analysis. We use hypothesis testing to test and validate assumptions in order to draw conclusions about a population from the sample data. Popular tests include Z-test, F-Test, ANOVA test and confidence intervals . On the other hand, regression analysis primarily estimates the relationship between a dependent variable and one or more independent variables. There are numerous types of regression analysis but the most popular ones include linear and logistic regression .  

Statistical Analysis Steps  

In the era of big data and data science, there is a rising demand for a more problem-driven approach. As a result, we must approach statistical analysis holistically. We may divide the entire process into five different and significant stages by using the well-known PPDAC model of statistics: Problem, Plan, Data, Analysis and Conclusion.

In the first stage, you define the problem you want to tackle and explore questions about the problem. 

Next is the planning phase. You can check whether data is available or if you need to collect data for your problem. You also determine what to measure and how to measure it. 

The third stage involves data collection, understanding the data and checking its quality. 

4. Analysis

Statistical data analysis is the fourth stage. Here you process and explore the data with the help of tables, graphs and other data visualizations.  You also develop and scrutinize your hypothesis in this stage of analysis. 

5. Conclusion

The final step involves interpretations and conclusions from your analysis. It also covers generating new ideas for the next iteration. Thus, statistical analysis is not a one-time event but an iterative process.

Statistical Analysis Uses

Statistical analysis is useful for research and decision making because it allows us to understand the world around us and draw conclusions by testing our assumptions. Statistical analysis is important for various applications, including:

• Statistical quality control and analysis in product development 
• Clinical trials
• Customer satisfaction surveys and customer experience research 
• Marketing operations management
• Process improvement and optimization
• Training needs 

More on Statistical Analysis From Built In Experts Intro to Descriptive Statistics for Machine Learning

Benefits of Statistical Analysis

Here are some of the reasons why statistical analysis is widespread in many applications and why it’s necessary:

Understand Data

Statistical analysis gives you a better understanding of the data and what they mean. These types of analyses provide information that would otherwise be difficult to obtain by merely looking at the numbers without considering their relationship.

Find Causal Relationships

Statistical analysis can help you investigate causation or establish the precise meaning of an experiment, like when you’re looking for a relationship between two variables.

Make Data-Informed Decisions

Businesses are constantly looking to find ways to improve their services and products . Statistical analysis allows you to make data-informed decisions about your business or future actions by helping you identify trends in your data, whether positive or negative. 

Determine Probability

Statistical analysis is an approach to understanding how the probability of certain events affects the outcome of an experiment. It helps scientists and engineers decide how much confidence they can have in the results of their research, how to interpret their data and what questions they can feasibly answer.

You’ve Got Questions. Our Experts Have Answers. Confidence Intervals, Explained!

What Are the Risks of Statistical Analysis?

Statistical analysis can be valuable and effective, but it’s an imperfect approach. Even if the analyst or researcher performs a thorough statistical analysis, there may still be known or unknown problems that can affect the results. Therefore, statistical analysis is not a one-size-fits-all process. If you want to get good results, you need to know what you’re doing. It can take a lot of time to figure out which type of statistical analysis will work best for your situation .

Thus, you should remember that our conclusions drawn from statistical analysis don’t always guarantee correct results. This can be dangerous when making business decisions. In marketing , for example, we may come to the wrong conclusion about a product . Therefore, the conclusions we draw from statistical data analysis are often approximated; testing for all factors affecting an observation is impossible.

Built In’s expert contributor network publishes thoughtful, solutions-oriented stories written by innovative tech professionals. It is the tech industry’s definitive destination for sharing compelling, first-person accounts of problem-solving on the road to innovation.

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Statistics Made Easy

The Importance of Statistics in Research (With Examples)

The field of statistics is concerned with collecting, analyzing, interpreting, and presenting data.

In the field of research, statistics is important for the following reasons:

Reason 1 : Statistics allows researchers to design studies such that the findings from the studies can be extrapolated to a larger population.

Reason 2 : Statistics allows researchers to perform hypothesis tests to determine if some claim about a new drug, new procedure, new manufacturing method, etc. is true.

Reason 3 : Statistics allows researchers to create confidence intervals to capture uncertainty around population estimates.

In the rest of this article, we elaborate on each of these reasons.

Reason 1: Statistics Allows Researchers to Design Studies

Researchers are often interested in answering questions about populations like:

• What is the average weight of a certain species of bird?
• What is the average height of a certain species of plant?
• What percentage of citizens in a certain city support a certain law?

One way to answer these questions is to go around and collect data on every single individual in the population of interest.

However, this is typically too costly and time-consuming which is why researchers instead take a  sample  of the population and use the data from the sample to draw conclusions about the population as a whole.

There are many different methods researchers can potentially use to obtain individuals to be in a sample. These are known as  sampling methods .

There are two classes of sampling methods:

• Probability sampling methods : Every member in a population has an equal probability of being selected to be in the sample.
• Non-probability sampling methods : Not every member in a population has an equal probability of being selected to be in the sample.

By using probability sampling methods, researchers can maximize the chances that they obtain a sample that is representative of the overall population.

This allows researchers to extrapolate the findings from the sample to the overall population.

Read more about the two classes of sampling methods here .

Reason 2: Statistics Allows Researchers to Perform Hypothesis Tests

Another way that statistics is used in research is in the form of hypothesis tests .

These are tests that researchers can use to determine if there is a statistical significance between different medical procedures or treatments.

For example, suppose a scientist believes that a new drug is able to reduce blood pressure in obese patients. To test this, he measures the blood pressure of 30 patients before and after using the new drug for one month.

He then performs a paired samples t- test using the following hypotheses:

• H 0 : μ after = μ before (the mean blood pressure is the same before and after using the drug)
• H A : μ after < μ before (the mean blood pressure is less after using the drug)

If the p-value of the test is less than some significance level (e.g. α = .05), then he can reject the null hypothesis and conclude that the new drug leads to reduced blood pressure.

Note : This is just one example of a hypothesis test that is used in research. Other common tests include a one sample t-test , two sample t-test , one-way ANOVA , and two-way ANOVA .

Reason 3: Statistics Allows Researchers to Create Confidence Intervals

Another way that statistics is used in research is in the form of confidence intervals .

A confidence interval is a range of values that is likely to contain a population parameter with a certain level of confidence.

For example, suppose researchers are interested in estimating the mean weight of a certain species of turtle.

Instead of going around and weighing every single turtle in the population, researchers may instead take a simple random sample of turtles with the following information:

• Sample size  n = 25
• Sample mean weight  x  = 300
• Sample standard deviation  s = 18.5

Using the confidence interval for a mean formula , researchers may then construct the following 95% confidence interval:

95% Confidence Interval:  300 +/-  1.96*(18.5/√ 25 ) =  [292.75, 307.25]

The researchers would then claim that they’re 95% confident that the true mean weight for this population of turtles is between 292.75 pounds and 307.25 pounds.

Additional Resources

The following articles explain the importance of statistics in other fields:

The Importance of Statistics in Healthcare The Importance of Statistics in Nursing The Importance of Statistics in Business The Importance of Statistics in Economics The Importance of Statistics in Education

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Department of Statistics - Donald Bren School of Information & Computer Sciences

What is statistics.

Statistics is the science concerned with developing and studying methods for collecting, analyzing, interpreting and presenting empirical data. Statistics is a highly interdisciplinary field; research in statistics finds applicability in virtually all scientific fields and research questions in the various scientific fields motivate the development of new statistical methods and theory. In developing methods and studying the theory that underlies the methods statisticians draw on a variety of mathematical and computational tools.

Two fundamental ideas in the field of statistics are uncertainty and variation. There are many situations that we encounter in science (or more generally in life) in which the outcome is uncertain. In some cases the uncertainty is because the outcome in question is not determined yet (e.g., we may not know whether it will rain tomorrow) while in other cases the uncertainty is because although the outcome has been determined already we are not aware of it (e.g., we may not know whether we passed a particular exam).

Probability is a mathematical language used to discuss uncertain events and probability plays a key role in statistics. Any measurement or data collection effort is subject to a number of sources of variation. By this we mean that if the same measurement were repeated, then the answer would likely change. Statisticians attempt to understand and control (where possible) the sources of variation in any situation.

We encourage you to continue exploring our website to learn more about statistics, our academic programs, our students and faculty, as well as the cutting-edge research we are doing in the field.

Table of Contents

Types of statistical analysis, importance of statistical analysis, benefits of statistical analysis, statistical analysis process, statistical analysis methods, statistical analysis software, statistical analysis examples, career in statistical analysis, choose the right program, become proficient in statistics today, what is statistical analysis types, methods and examples.

Statistical analysis is the process of collecting and analyzing data in order to discern patterns and trends. It is a method for removing bias from evaluating data by employing numerical analysis. This technique is useful for collecting the interpretations of research, developing statistical models, and planning surveys and studies.

Statistical analysis is a scientific tool in AI and ML that helps collect and analyze large amounts of data to identify common patterns and trends to convert them into meaningful information. In simple words, statistical analysis is a data analysis tool that helps draw meaningful conclusions from raw and unstructured data. 

The conclusions are drawn using statistical analysis facilitating decision-making and helping businesses make future predictions on the basis of past trends. It can be defined as a science of collecting and analyzing data to identify trends and patterns and presenting them. Statistical analysis involves working with numbers and is used by businesses and other institutions to make use of data to derive meaningful information. 

Given below are the 6 types of statistical analysis:

Descriptive Analysis

Descriptive statistical analysis involves collecting, interpreting, analyzing, and summarizing data to present them in the form of charts, graphs, and tables. Rather than drawing conclusions, it simply makes the complex data easy to read and understand.

Inferential Analysis

The inferential statistical analysis focuses on drawing meaningful conclusions on the basis of the data analyzed. It studies the relationship between different variables or makes predictions for the whole population.

Predictive Analysis

Predictive statistical analysis is a type of statistical analysis that analyzes data to derive past trends and predict future events on the basis of them. It uses machine learning algorithms, data mining , data modelling , and artificial intelligence to conduct the statistical analysis of data.

Prescriptive Analysis

The prescriptive analysis conducts the analysis of data and prescribes the best course of action based on the results. It is a type of statistical analysis that helps you make an informed decision. 

Exploratory Data Analysis

Exploratory analysis is similar to inferential analysis, but the difference is that it involves exploring the unknown data associations. It analyzes the potential relationships within the data. 

Causal Analysis

The causal statistical analysis focuses on determining the cause and effect relationship between different variables within the raw data. In simple words, it determines why something happens and its effect on other variables. This methodology can be used by businesses to determine the reason for failure. 

Statistical analysis eliminates unnecessary information and catalogs important data in an uncomplicated manner, making the monumental work of organizing inputs appear so serene. Once the data has been collected, statistical analysis may be utilized for a variety of purposes. Some of them are listed below:

• The statistical analysis aids in summarizing enormous amounts of data into clearly digestible chunks.
• The statistical analysis aids in the effective design of laboratory, field, and survey investigations.
• Statistical analysis may help with solid and efficient planning in any subject of study.
• Statistical analysis aid in establishing broad generalizations and forecasting how much of something will occur under particular conditions.
• Statistical methods, which are effective tools for interpreting numerical data, are applied in practically every field of study. Statistical approaches have been created and are increasingly applied in physical and biological sciences, such as genetics.
• Statistical approaches are used in the job of a businessman, a manufacturer, and a researcher. Statistics departments can be found in banks, insurance businesses, and government agencies.
• A modern administrator, whether in the public or commercial sector, relies on statistical data to make correct decisions.
• Politicians can utilize statistics to support and validate their claims while also explaining the issues they address.

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Statistical analysis can be called a boon to mankind and has many benefits for both individuals and organizations. Given below are some of the reasons why you should consider investing in statistical analysis:

• It can help you determine the monthly, quarterly, yearly figures of sales profits, and costs making it easier to make your decisions.
• It can help you make informed and correct decisions.
• It can help you identify the problem or cause of the failure and make corrections. For example, it can identify the reason for an increase in total costs and help you cut the wasteful expenses.
• It can help you conduct market analysis and make an effective marketing and sales strategy.
• It helps improve the efficiency of different processes.

Given below are the 5 steps to conduct a statistical analysis that you should follow:

• Step 1: Identify and describe the nature of the data that you are supposed to analyze.
• Step 2: The next step is to establish a relation between the data analyzed and the sample population to which the data belongs. 
• Step 3: The third step is to create a model that clearly presents and summarizes the relationship between the population and the data.
• Step 4: Prove if the model is valid or not.
• Step 5: Use predictive analysis to predict future trends and events likely to happen. 

Although there are various methods used to perform data analysis, given below are the 5 most used and popular methods of statistical analysis:

Mean or average mean is one of the most popular methods of statistical analysis. Mean determines the overall trend of the data and is very simple to calculate. Mean is calculated by summing the numbers in the data set together and then dividing it by the number of data points. Despite the ease of calculation and its benefits, it is not advisable to resort to mean as the only statistical indicator as it can result in inaccurate decision making. 

Standard Deviation

Standard deviation is another very widely used statistical tool or method. It analyzes the deviation of different data points from the mean of the entire data set. It determines how data of the data set is spread around the mean. You can use it to decide whether the research outcomes can be generalized or not. 

Regression is a statistical tool that helps determine the cause and effect relationship between the variables. It determines the relationship between a dependent and an independent variable. It is generally used to predict future trends and events.

Hypothesis Testing

Hypothesis testing can be used to test the validity or trueness of a conclusion or argument against a data set. The hypothesis is an assumption made at the beginning of the research and can hold or be false based on the analysis results. 

Sample Size Determination

Sample size determination or data sampling is a technique used to derive a sample from the entire population, which is representative of the population. This method is used when the size of the population is very large. You can choose from among the various data sampling techniques such as snowball sampling, convenience sampling, and random sampling. 

Everyone can't perform very complex statistical calculations with accuracy making statistical analysis a time-consuming and costly process. Statistical software has become a very important tool for companies to perform their data analysis. The software uses Artificial Intelligence and Machine Learning to perform complex calculations, identify trends and patterns, and create charts, graphs, and tables accurately within minutes. 

Look at the standard deviation sample calculation given below to understand more about statistical analysis.

The weights of 5 pizza bases in cms are as follows:

Calculation of Mean = (9+2+5+4+12)/5 = 32/5 = 6.4

Calculation of mean of squared mean deviation = (6.76+19.36+1.96+5.76+31.36)/5 = 13.04

Sample Variance = 13.04

Standard deviation = √13.04 = 3.611

A Statistical Analyst's career path is determined by the industry in which they work. Anyone interested in becoming a Data Analyst may usually enter the profession and qualify for entry-level Data Analyst positions right out of high school or a certificate program — potentially with a Bachelor's degree in statistics, computer science, or mathematics. Some people go into data analysis from a similar sector such as business, economics, or even the social sciences, usually by updating their skills mid-career with a statistical analytics course.

Statistical Analyst is also a great way to get started in the normally more complex area of data science. A Data Scientist is generally a more senior role than a Data Analyst since it is more strategic in nature and necessitates a more highly developed set of technical abilities, such as knowledge of multiple statistical tools, programming languages, and predictive analytics models.

Aspiring Data Scientists and Statistical Analysts generally begin their careers by learning a programming language such as R or SQL. Following that, they must learn how to create databases, do basic analysis, and make visuals using applications such as Tableau. However, not every Statistical Analyst will need to know how to do all of these things, but if you want to advance in your profession, you should be able to do them all.

Based on your industry and the sort of work you do, you may opt to study Python or R, become an expert at data cleaning, or focus on developing complicated statistical models.

You could also learn a little bit of everything, which might help you take on a leadership role and advance to the position of Senior Data Analyst. A Senior Statistical Analyst with vast and deep knowledge might take on a leadership role leading a team of other Statistical Analysts. Statistical Analysts with extra skill training may be able to advance to Data Scientists or other more senior data analytics positions.

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Hope this article assisted you in understanding the importance of statistical analysis in every sphere of life. Artificial Intelligence (AI) can help you perform statistical analysis and data analysis very effectively and efficiently. 

If you are a science wizard and fascinated by the role of AI in statistical analysis, check out this amazing Caltech Post Graduate Program in AI & ML course in collaboration with Caltech. With a comprehensive syllabus and real-life projects, this course is one of the most popular courses and will help you with all that you need to know about Artificial Intelligence. 

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Get facts and insights on topics that matter, mar 24, 2024 | currencies, bitcoin btc/usd price history up until mar 23, 2024.

Bitcoin (BTC) price again reached an all-time high in 2021, as values exceeded over 65,000 USD in November 2021. That particular price hike was connected to the launch of a Bitcoin ETF in the United States, whilst others in 2021 were due to events involving Tesla and Coinbase, respectively. Tesla's announcement in March 2021 that it had acquired 1.5 billion U.S. dollars' worth of the digital coin, for example, as well as the IPO of the U.S.' biggest crypto exchange fueled mass interest. The market was noticably different by the end of 2022, however, with Bitcoin prices reaching roughly 63,509.04 as of March 23, 2024 after another crypto exchange, FTX, filed for bankruptcy. Is the world running out of Bitcoin?Unlike fiat currency like the U.S. dollar - as the Federal Reserve can simply decide to print more banknotes - Bitcoin's supply is finite: BTC has a maximum supply embedded in its design , of which roughly 89 percent had been reached in April 2021. It is believed that Bitcoin will run out by 2040, despite more powerful mining equipment. This is because mining becomes exponentially more difficult and power-hungry every four years, a part of Bitcoin's original design. Because of this, a Bitcoin mining transaction could equal the energy consumption of a small country in 2021. Bitcoin's price outlook: a potential bubble?Cryptocurrencies have few metrices available that allow for forecasting, if only because it is rumored that only few cryptocurrency holders own a large portion of available supply. These large holders - referred to as 'whales' - are said to make up of two percent of anonymous ownership accounts, whilst owning roughly 92 percent of BTC. On top of this, most people who use cryptocurrency-related services worldwide are retail clients rather than institutional investors. This means outlooks on whether Bitcoin prices will fall or grow are difficult to measure, as movements from one large whale already having a significant impact on this market.

Mar 20, 2024 | B2C E-Commerce

Global online shopper per visit spend 2023, by category.

In the fourth quarter of 2023, online shoppers spent an average of about 2.95 U.S. dollars per visit across all verticals. Home furniture is the category in which consumers spent the most money per visit on average, at 3.41 U.S. dollars, followed by luxury apparel at 3.24 dollars. Nickels and dimes Over the past few years, the average order value for e-commerce purchases has increased globally, from around 118 U.S. dollars in September 2022 to around 126 U.S. dollars in the same month of 2023. The average order value also depends heavily on the online traffic source consumers use. In 2023, the value per order value was the highest when navigating directly, averaging around 167 dollars. Direct navigation means searching for a website directly in the browser's address bar, bypassing the use of search engines. Orders placed from social media stores were the lowest in value, with an average of less than 110 dollars. Mobile shopping on the rise Online shoppers have clear preferences when it comes to device type. When comparing gadgets , the average purchase amount has always been the highest for desktops, with an order value of 170 U.S. dollars. This indicates that bigger purchases are made via desktop computers. However, consumers are more likely to complete orders when shopping on mobile devices. Mobile devices were also clearly preferred when browsing retail websites , with around three-fourths of consumers using smartphones instead of desktops or tablets.

Mar 19, 2024 | Apparel & Shoes

Global brand value comparison of nike and adidas from 2010 to 2023.

The brand value of Nike has increased year-on-year since 2010 and reached over 53 billion U.S. dollars in 2023. In comparison, the adidas brand was valued at approximately 16.6 billion U.S. dollars in – increasing for the eight consecutive year following two years of decline. Will there be a power shift in North America? With instantly recognizable logos and catchy slogans, Nike and adidas are the two biggest sportswear brands, and Nike is one of the most valuable brands worldwide . Both companies are driving growth in the sportswear market worldwide, but Nike has a particularly firm hold of its home market of North America: the company generates over 40 percent of its total revenue from the region, whereas around a quarter of adidas’ global revenue comes from North America. The stories behind the logos Nike has its swoosh; adidas has its three stripes. Those logos have become synonymous with the brands they represent, and consumers will pay a higher price to wear products with the logos emblazoned on them. The Nike swoosh was created by graphic design student Carolyn Davidson in 1971, who was paid a fee of 35 U.S. dollars. The three stripes trademark was bought by adidas from another sportswear company in the early 1950s. The design used to belong to a Finnish brand called Karhu, but they sold the logo for a small fee that included two bottles of whiskey.

Mar 19, 2024 | Elections

U.s. voters most important issue 2024, by party.

According to a survey from March 2024, the most important issue among Republican voters in the United States was immigration, with 33 percent ranking it as their primary political concern. In contrast, only five percent of Democrats considered immigration their most important issue. Inflation and prices was the leading issue among democrats in the U.S.

Mar 20, 2024 | Traditional advertising

Out-of-home (ooh) advertising revenue in the u.s. 2007-2023.

In 2023, out-of-home (OOH) advertising revenue in the United States grew by about two percent to approximately 8.73 billion U.S. dollars. That was the first time the annual figure exceeded the 8.56 billion-dollar spending recorded in 2019, before the COVID-19 outbreak – lockdowns in response to the pandemic led to a historic drop in the outdoor ad expenditure. Digital and outdoors The evolution of outdoor ads comes with the rising digital out-of-home (DOOH) media trend. According to another source, DOOH's share in total OOH ad spend in the U.S. will go from less than one-third in 2022 to more than 41 percent by 2026. In the meantime, the market expects the U.S. DOOH ad spend to expand accordingly. The annual figure was projected to surpass three billion dollars in 2024 and reach almost 3.6 billion dollars two years later. Multiplying and innovating The increase in digital outdoor ads is palpable. Between 2016 and 2022, the number of digital billboards spread across the U.S. skyrocketed by about 80 percent, amounting to 11.5 thousand in the latter year. The boom attracts brands while offering new possibilities in product and service promotion. During a late 2022 survey among agency and advertising executives, almost half of them listed DOOH among the media developing the most innovative opportunities for advertisers in the U.S.

Mar 20, 2024 | Social Media & User-Generated Content

Number of active wechat messenger accounts q4 2013-q4 2023.

The number of Tencent's WeChat active accounts has been increasing steadily. As at the end of December 2023, the Chinese multi-functional social media platform had over 1.3 billion monthly active users.  WeChat users First released in 2011, WeChat is a mobile messaging app developed by the Chinese company Tencent. In its home market of China, WeChat was marketed as Weixin and was rebranded as WeChat in 2012 for international audiences. In 2018, WeChat and Weixin surpassed one billion users, which was a significant increase from the previous year. Today, WeChat is one of the leading social networks worldwide , ranking fifth in terms of active user number. It has users from different age groups. Special features on WeChat WeChat has lots of popular messaging app features, including Moments. A majority of WeChat users access WeChat Moments every time they open the app. Voice and text messaging, group messaging, payment, and games are other examples of WeChat activities. The app also includes a following function whereby users can follow accounts. A survey found that a quarter of WeChat users in China spent over four hours on the app on a daily basis.

Mar 21, 2024 | Elections

Results for the presidential election indonesia 2024.

Official results from Indonesia's national election conducted on February 14, 2024, were released on March 20, 2024. The presidential and vice presidential candidates Prabowo Subianto and Gibran Rakabuming Raka received the highest share of votes, amounting to 58.58 percent. The history of Indonesia’s democratic election Indonesia holds a national election every five years to choose a new president, vice president, and parliamentary and local representatives. This year’s event marks only the fifth presidential election in Indonesian history . Indonesia’s first presidential election occurred in 2004, as the country shifted into the Reform era after the New Order era ended, marked by the fall of President Soeharto’s three-decade rule in 1998. Before 2004, Indonesia’s president was directly appointed by the People’s Consultative Assembly (Majelis Permusyawaratan Rakyat/MPR). Young voters of Indonesia  Millennial and Gen Z voters made up more than half of Indonesia’s 204 million registered voters in the 2024 election , thus playing a significant role in the electoral scene. Social media platforms like TikTok, Instagram, and YouTube have become key sources for obtaining political information among these young voters. Notably, the three presidential candidates spent billions of Indonesian rupiah to conduct advertising campaigns through Meta platforms . 

Mar 21, 2024 | Population

Forecast of the total population of africa 2020-2050.

According to the forecast, Africa's total population would reach nearly 2.5 billion by 2050. In 2023, the continent had around 1.36 billion inhabitants, with Nigeria, Ethiopia, and Egypt as the most populous countries . In the coming years, Africa will experience significant population growth and will close the gap significantly with the Asian population by 2100.

The population of Africa has been increasing annually in recent years, growing from around 818 million to over 1.39 billion between 2000 and 2021, respectively. In the same period, the annual growth rate of the population has been constantly set at roughly 2.5 percent, with a peak of 2.62 percent in 2014. The reasons behind this rapid growth are various. One factor is the high fertility rate registered in African countries. In 2021, a woman in Niger had an average of over 6.8 children in her reproductive years, the highest rate on the continent. High fertility resulted in a large young population and partly compensated for the high mortality rate in Africa, leading to fast-paced population growth. 

Africa’s population is concerned with widespread poverty. In 2024, over 429 million people on the continent are extremely poor and live with less than 2.15 U.S. dollars per day. Globally, Africa is the continent hosting the highest poverty rate. In 2024, the countries of Nigeria and the Democratic Republic of the Congo account for around 21 percent of the  world's population living in extreme poverty . Nevertheless, poverty in Africa is forecast to decrease in the coming years.

Mar 21, 2024 | Vehicles & Road Traffic

Global revenue of bmw group 2007-2023.

In 2023, BMW Group’s global revenue stood at around 155.5 billion euros. The German vehicle manufacturer sells vehicles under the BMW, Rolls-Royce, and MINI brands and was among the leading luxury car brands worldwide in 2022. Following the financial disaster of 2008-2009, BMW recovered rather quickly, surpassing pre-crash revenue and EBIT by 2010. BMW in the United States BMW is currently the third best-selling European automotive brand in the United States, with quarterly sales reaching nearly 107,900 units in the fourth quarter of 2023. The MINI brand, however, struggles to sell in the U.S. but MINI Cooper has done well to stay in the U.S. market, which is in part due to the Countryman model that accounted for 40.2 percent of Mini’s U.S. sales in 2018. Electrification BMW has announced ambitious plans for electric vehicle deliveries to make up half of its total deliveries by 2030. If successful, this should increase the company’s electric passenger car market share in Europe that currently stands at one percent. Full electrification awaits the company’s MINI brand, and the company may even have ambitious ideas for fully electric, high-performance sports cars and motorbikes. The global electric fleet-size is expected to grow by over fourfold to 17.07 million vehicles in 2028.

Mar 20, 2024 | B2C Services

Main food delivery apps in the uk 2023, by downloads.

With over twelve million downloads, Tesco Groceries was by far the most popular food delivery app in the United Kingdom in 2023. The second place went to Just Eat, which recorded about 2.4 million downloads that year. Deliveroo followed third, with over 2.3 million downloads.

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Statistical and data sciences professor uses structural equation modeling to show the importance of relationships in STEM degree persistence

Jillian Morrison, assistant professor of statistical and data sciences at The College of Wooster, was featured in the “Meet a Member” column of the February/March 2024 issue of MAA Focus, a publication of the Mathematical Association of America. Morrison discussed her passions in her work, including her recently published research on how social groups influence science, technology, engineering, and mathematics (STEM) majors’ persistence in completing their degree. She collaborated with Aubrey Whitehead, assistant professor of psychology at the Virginia Military Institute and former Perry-Williams postdoctoral fellow of psychology at Wooster, and Melissa Schen, director of educational assessment at Wooster, on the study, which was published in the Journal for STEM Education Research in June 2023.

“We wanted to identify the factors that motivate students to remain in STEM and help them to persist,” Morrison said. “This is important because we’re getting more and more STEM graduates from liberal arts colleges, and these colleges must be able to foster the environment that students in STEM need to be successful.”

The researchers surveyed 295 STEM majors from a small liberal arts college across all four years about how close social groups, including family members, friends, and professors, influenced their motivation to attain their degree. They then tested a hypothesized structural equation model to determine the subgroups that the students perceived most affected their degree persistence and motivational factors, such as self-efficacy and interest.

“The structural equation model, in simple terms, helped us identify these relationships between the dataset. What we found was that fathers, mothers, and professors have a significant role to play when it comes to students persisting in STEM,” said Morrison. “Often, we think students are most motivated by their friends, but that’s not all our data shows. Our data shows that up through the students’ senior year, parents and professors are also playing a huge role.”

The findings have implications for colleges that are looking to find ways to better support STEM students. “If we know who’s helping motivate them and who’s helping them to persist, then we can build programs and structures that help foster those relationships,” Morrison said. “At a small liberal arts college like Wooster, that may mean thinking about ways to support strong relationships between family members and STEM students. That could be inviting family members to events more often, either virtually or in person, and encouraging them to be huge cheerleaders. It also means supporting relationships between professors and STEM students by investing in faculty and hiring good professors. We want to make sure that the support system is there for these students.”

Morrison notes that by fostering these key relationships, colleges can support students both during their academic career and beyond. “If you look at statistics and data science jobs and the potential for careers in these fields in the coming years, we’re expecting exponential growth,” she said. “We need to ensure that we’re able to support students as they progress not only through their education, but also in their future careers. We want to help them become lifelong learners.”

In Morrison’s current research, she involves students in a variety of ways from conducting literature reviews to calculating sample sizes to creating R packages. “I love working with students and involving them in my research,” said Morrison, noting that Wooster’s STEM programs offer a number of similar research opportunities as well as support programs. “With anything that I work on, I try to find ways to involve students. I’m passionate about helping people figure out who they want to be and inspiring them to be the best version of themselves.”

Posted in Faculty , News on April 1, 2024.

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OMB Publishes Revisions to Statistical Policy Directive No. 15: Standards for Maintaining, Collecting, and Presenting Federal Data on Race and   Ethnicity

By Dr. Karin Orvis, Chief Statistician of the United States

Earlier today, OMB published a set of revisions to Statistical Policy Directive No. 15 (Directive No. 15): Standards for Maintaining, Collecting, and Presenting Federal Data on Race and Ethnicity, the first since 1997. This process started in June 2022 , with the first convening of the Interagency Technical Working Group of Federal Government career staff who represent programs that collect or use race and ethnicity data. Since that first convening, we’ve reviewed 20,000 comments and held almost 100 listening sessions to finalize the important standards we are announcing today.

Thanks to the hard work of staff across dozens of federal agencies and input from thousands of members of the public, these updated standards will help create more useful, accurate, and up to date federal data on race and ethnicity. These revisions will enhance our ability to compare information and data across federal agencies, and also to understand how well federal programs serve a diverse America.

You can read the updated Directive No. 15 on the Federal Register as well as at www.spd15revision.gov .

The Process

In June 2022, OMB convened the Interagency Technical Working Group on Race and Ethnicity Standards (Working Group) to develop recommendations for improving the quality and usefulness of federal race and ethnicity data.

The Working Group, consisting of federal government career staff representing 35 agencies, relied heavily on research conducted over the last decade, including new research and testing of potential alternatives by several federal agencies. The Working Group also relied on robust public input, including:

• over 20,000 comments provided in response to a January 2023 Federal Register Notice proposing revisions to the Directive,
• 94 listening sessions hosted by the Working Group,
• 3 public virtual townhalls , and a Tribal consultation.

Informed by these perspectives, the Working Group delivered a thoughtful and data-driven report to OMB with recommendations for updating and improving Directive No. 15.

The Revisions

The Working Group’s final recommendations included several critical revisions that have been thoroughly researched and tested over the last decade. The updated standards released by OMB today closely follow the Working Group’s evidence-based recommendations and make key revisions to questions used to collect information on race and ethnicity, including:

• Using one combined question for race and ethnicity, and encouraging respondents to select as many options as apply to how they identify.
• American Indian or Alaska Native
• Black or African American
• Hispanic or Latino
• Middle Eastern or North African
• Native Hawaiian or Pacific Islander
• Requiring the collection of additional detail beyond the minimum required race and ethnicity categories for most situations, to ensure further disaggregation in the collection, tabulation, and presentation of data when useful and appropriate.

The updated standards also include several additional updates to definitions, terminology, and guidance to agencies on the collection and presentation of data.

Now What Happens?

One of the primary goals of Directive No. 15 is to ensure consistent and comparable race and ethnicity data across the federal government. To help meet that goal, the standards instruct federal agencies to begin updating their surveys and administrative forms as quickly as possible, submit an Agency Action Plan for complete compliance within 18 months – which will be publicly available, and finish bringing all data collections and programs into compliance with the updated standards within five years of today’s date. However, many programs will be able to adopt the updated standards much sooner than that. Starting today, the Office of the U.S. Chief Statistician will direct its efforts to help agencies collect and release data under these updated standards as quickly as possible.

In addition, this review process showed that racial and ethnic identities, concepts, and data needs continue to evolve. To improve the ability of Directive No. 15 to adapt and better meet those needs, OMB is establishing an Interagency Committee on Race and Ethnicity Statistical Standards, convened by the Office of the U.S. Chief Statistician, that will maintain and carry out a government-wide research agenda and undertake regular reviews of Directive No. 15. Some areas of interest identified in the technical expert research, as well as by stakeholders and engaged members of the public, lacked sufficient data to determine the effects of potential changes. Those areas of interest have now been identified as a top priority for additional research and data development in advance of future reviews. The updated standards identify several key research topics for the Interagency Committee to focus on initially. For more information on these research topics and the planned schedule for future reviews, see the updated Directive No. 15.

This monumental effort was informed by the perspectives of staff across federal agencies and the members of the public who took the time to submit written comments, provide views at one of the virtual town halls, meet with the Working Group, and participate in our Tribal consultation. We are committed to maintaining a collaborative approach as we work to implement these new revisions.

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A Mathematician Uses AI to Find Meaning in Genomic Data

Imagine a murmuration of starlings on the wing—the flock wheels and turns as though a single organism—rising, falling, shifting in shape and form in response to cues undiscernible to the casual viewer. Zoom in and the independent behaviors of each bird that contribute to the flock’s coordinated movements become clear.

So, too, the genes in each of our cells operate in networks, activated or silenced by RNA in response to a wide array of environmental cues, at scales from single proteins within the cytoplasm of the cell, itself, to conditions within each tissue type and even the organism as a whole. Columbia Mailman School Assistant Professor of Biostatistics Wenpin Hou uses machine learning to analyze massive datasets from human tissue samples to reveal the biochemical activity within single cells. “My goal is to extract meaningful information from real-world, messy data using computational methods and software to drive forward the understanding of how and when the genes are regulated, and how we can control gene regulation in disease prevention and treatment,” she says.

Hou’s work has already yielded multiple open-source software programs. Her latest, GPTCelltype , applies artificial intelligence to the laborious process of manually annotating the array of cell types found in complex tissue samples for single-cell RNA analyses.

You combine theoretical mathematics and statistics with emerging computational methods, machine learning, and artificial intelligence to investigate how our genes respond to our environment. How did you get started?

Hou: In my undergrad studies, I noticed the elegant ways that differential equations could capture gene behaviors. That sparked a deep dive into gene regulatory networks for my PhD. The beauty of mathematics applied to biology was irresistible. I was amazed by how artfully mathematics could capture those delicate gene dynamics and regulations.

How did you make the jump from theoretical analyses to applied biomedical and public health research?

Hou: During my PhD, I worked on the theory behind modeling gene regulatory networks, intending to find gene therapies. Later, at MD Anderson Cancer Center, I worked to develop this concept for cancer therapies, but the stark reality of noisy and incomplete data stood in the way. This challenge steered me from theory to hands-on computational genomics. I discovered a passion for solving real health problems and realized the impact my research could have on disease therapy and prevention—a truly pivotal experience.

Why can’t theoretical computational models lead directly to clinical applications?

Hou: Theoretical models often assume “clean” data, which is in stark contrast to the complexity and noise inherent in real-world patient data. This discrepancy creates significant challenges when attempting to apply these models clinically. By integrating various types of gene regulation data—including gene expression, DNA methylation, histone modification, transcription factor activity, and targeted perturbations—we have the potential to bridge this gap and catalyze the creation of new therapies.

You currently lead two five-year, NIH-funded projects that have been awarded nearly $1.9 million. Each investigates the spatial landscapes of DNA methylation and gene regulatory networks. What does this mean?

Hou: DNA methylation— an epigenetic modification that can modulate gene expression—stands at the interface of genome, environment, and development. Detailing how that process unfolds in space and time to determine gene expression could contain valuable target information for early diagnosis and drug treatment. My goal is to develop methods that can predict DNA methylation landscapes and create maps of the spatial relationships among those cells.

Can you provide an example from your ongoing work on maternal-fetal health?

Hou: I approached pediatrician and molecular epidemiologist Dr. Xiaobin Wang in 2019 to work with her on the Boston Birth Cohort, which combines demographic data and DNA methylation data. I’ve been able to contribute to a series of analyses of how maternal smoking affects genes associated with overweight and obesity in children, and how certain prenatal vitamins may buffer the effects of maternal smoking on newborn gene expression.

ChatGPT is in the news. You’ve looked at using GPT AI models for biomedical research. What do you see as the power and pitfalls of AI in your line of work?

Hou: If the machine can do well in this kind of task, it has the potential to equip experts and increase the efficiency of the work in the pipeline. On the other hand, humans provide our input to GPT-4, and the way we prompt it—the way we convey our instructions—affects the results, especially when we try to apply GPT-4 to large datasets. We recommend that human experts confirm the validity of the output from GPT-4.

You cofounded a statistical genetics and genomics working group that sponsors an ongoing seminar series. Who can participate?

Hou: We bring together faculty, fellows, and grad students from biostatistics, computational biology, engineering, and medicine from both inside and beyond Columbia to discuss broad applications of methodologies for investigating how the genome and genes affect biological function and human health. All of our invited talks are hosted virtually, and the public is welcome to attend. We discuss the latest progress in the field, including new statistical and computational methodology, new data resources in genetics and genomics, and new bioengineering technologies.

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Research: How Different Fields Are Using GenAI to Redefine Roles

• Maryam Alavi

Examples from customer support, management consulting, professional writing, legal analysis, and software and technology.

The interactive, conversational, analytical, and generative features of GenAI offer support for creativity, problem-solving, and processing and digestion of large bodies of information. Therefore, these features can act as cognitive resources for knowledge workers. Moreover, the capabilities of GenAI can mitigate various hindrances to effective performance that knowledge workers may encounter in their jobs, including time pressure, gaps in knowledge and skills, and negative feelings (such as boredom stemming from repetitive tasks or frustration arising from interactions with dissatisfied customers). Empirical research and field observations have already begun to reveal the value of GenAI capabilities and their potential for job crafting.

There is an expectation that implementing new and emerging Generative AI (GenAI) tools enhances the effectiveness and competitiveness of organizations. This belief is evidenced by current and planned investments in GenAI tools, especially by firms in knowledge-intensive industries such as finance, healthcare, and entertainment, among others. According to forecasts, enterprise spending on GenAI will increase by two-fold in 2024 and grow to $151.1 billion by 2027 .

• Maryam Alavi is the Elizabeth D. & Thomas M. Holder Chair & Professor of IT Management, Scheller College of Business, Georgia Institute of Technology .

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Statistical Advances in Epidemiology and Public Health

Domenica matranga.

1 Department of Health Promotion, Mother and Child Care, Internal Medicine and Medical Specialties, University of Palermo, 90127 Palermo, Italy

Filippa Bono

2 Department of Economics, Business and Statistics, University of Palermo, 90128 Palermo, Italy; [email protected]

Laura Maniscalco

3 Department of Biomedicine, Neuroscience and Advanced Diagnostics, University of Palermo, 90127 Palermo, Italy; [email protected]

Associated Data

No new data were created or analyzed in this study. Data sharing is not applicable to this article.

The key role of statistical modeling in epidemiology and public health is unquestionable. The methods and tools of biostatistics are extensively used to understand disease development, uncover the etiology, and evaluate the development of new strategies of prevention and control of the disease. Through data analysis, epidemiology can steer decision-making processes, guide health and healthcare policy, and plan and assist in the management and care of health and disease in individuals. The growing availability of large healthcare databases allows drawing new evidence in the use of healthcare interventions, drugs, and devices, and in the knowledge of population health and health inequality. Clinical decisions grounded on evidence of efficacy and safety of medical interventions can contribute to prolonging people’s lives, improving their quality, and promoting appropriateness [ 1 ].

The present Special Issue focuses on statistical methods applied to epidemiology and public health. It is advisable to enlarge the evidence-based approach to a value-based approach by including the quality and the costs dimensions from both the demand and the supply of healthcare services [ 2 ]. Thus, particular attention has also been given to the evaluation and the cost-effectiveness of procedures and services. This special issue includes twelve articles from research teams worldwide, specifically Belgium, Spain, China, Canada, Austria, and Romania, apart from Italy. These studies focus on epidemiology, public health, and health promotion [ 3 , 4 , 5 , 6 ], suggest appropriate statistical methodologies for specific research questions [ 7 , 8 , 9 , 10 , 11 ], and analyze the quality of services provided to patients [ 12 , 13 , 14 ].

Of the four articles in the field of public health, two studies focus on disease prevention and health promotion strategies. In detail, Matranga et al. aim to assess whether the adoption of healthy behaviors could be significantly associated with psychological well-being in a cohort of students in the healthcare area [ 3 ]. This study shows that students inclined to well-being consider healthcare professionals as models for their patients and all people in general. Furthermore, the positive relationship between a virtuous lifestyle and psychological well-being suggests the construction, development, and growth of individual skills to counteract unhealthy behaviors. The study of Fujihara et al. addresses the issue of health promotion turning to a different target [ 4 ]. Through a prospective cohort design on older people in Japan, it focuses on examining the possible association between community-level social capital and the incidence of Instrumental activities of daily living (IADL) disability. The outcomes obtained from the present investigation show that community-based interventions that are finalized to promote community-level social capital could help preventing IADL disability or reducing its incidence.

In the field of occupational epidemiology, the Special Issue includes two studies regarding job exposures, and one of these uses large databases [ 5 ]. Specifically, Maniscalco et al. investigate to what extent a change in employment contributes to cardiovascular, musculoskeletal, and neuropsychological health [ 5 ]. Using a sample of 10,530 Belgian workers in a seven-year follow-up study period, the authors find an increased risk of cardiovascular disease and a psychosocial load in association with a job change experience. The issue of students’ professional exposures has been considered by Verso et al. [ 6 ]. The authors scrutinize the prevalence of latent tuberculosis infection (LTBI) among undergraduate and postgraduate students in the healthcare area at the University of Palermo, Italy, and investigate about the occupational risk of infection among students. This study shows very few cases of LTBI, confirming that the incidence of LTBI is low among Italian students. Not only healthcare workers, but also healthcare students involved in traineeships, are daily exposed to the occupational risk of contracting tuberculosis, due to close and prolonged contact with patients. Effective prevention strategies are mandatory for University hospitals.

Regarding the articles of this Special Issue that are focused on statistical methodology, we advocate reading the study of Trivelli et al. [ 7 ] describing the spatio-temporal distribution of cardiovascular mortality, and the study of Maniscalco et al. [ 8 ] suggesting a data-driven approach to investigate the interrelationship among health indicators. These two papers give important contributions in the context of environmental epidemiology and health promotion. Specifically, the study of Trivelli et al. aims to determine the spatio-temporal association between environmental exposure to particulate matter, PM2.5 µg/m 3 , and the risk of cardiovascular mortality in the 2010–2015 period in an Italian region with a high level of air pollution and human activities, using a Bayesian smoothed approach [ 7 ]. Such an approach consists of a hierarchical mixed log-linear model with a Besag, York, and Mollié (BYM) random component in a fully Bayesian framework. As described by the authors in detail, this model produces a smoothed map of relative risks (RR) and allows extra-Poisson variability induced by the spatio-temporal data structure. The proposed model includes two Gaussian random effects, one spatially unstructured (exchangeable or white noise in the lattice) and one spatially structured (conditional autoregressive based on a Gaussian–Markov random field in the lattice). Through the smoothed maps of RRs, the authors show that the distribution of estimated risk of death for cardiovascular diseases did not change across the years between 2010 and 2015, and show evidence of three clusters of high-risk for cardiovascular diseases in Lomellina area in all the studied years.

The study of Maniscalco et al. in the field of health promotion attempts to investigate the interrelationship among statistical indicators, which are typically used to capture health multidimensionality of elderly people [ 8 ]. They are self-perceived health (SPH), quality of life (QoL) in older ages, chronic or non-communicable diseases (NCDs), global activity limitation, lifestyle, and cognitive functioning. The authors analyze data of people aged 50 or above, living in twenty-seven European countries and Israel, extracted from the Survey on Health, Ageing, and Retirement in Europe (SHARE). Through additive Bayesian network modeling, the authors consider all the indicators jointly and identify all direct and indirect relationships between them. Three directed acyclic graphs for each one of Spain, Italy, and Greece show SPH significantly associated with cognitive functioning and QoL of people aged 50 and above, and confirm the well-known association with chronic diseases. Two of the studies composing the Special Issue contribute in the field of generating real-world evidence, through a census of the available healthcare utilization databases [ 9 ] and through a study that accounts for immortal time bias in observational clinical studies [ 10 ]. These two papers supply an example of the importance of the information provided by healthcare administrative databases, which can be used in many fields of pharmacoepidemiology, such as safety, efficacy, and cost–efficacy of therapeutic strategies.

Specifically, Skrami et al. perform a census of the available Healthcare Utilization databases (HUD) across 19 Italian regions and two autonomous Trento and Bolzano provinces [ 9 ]. This paper is worthy of note, as many studies use data from HUDs to produce scientific evidence about the safety and efficacy profile of drugs. However, in order to combine the HUDs and compare diagnostic and therapeutic care pathways (PDTA) and protocols among different Italian regions, it is essential that the HUDs are harmonized and their information comparable. The work of Skrami et al. counts 352 HUDs between January 2014 and October 2016 that cover the whole population of a single region and recorded local-level data referred to the healthcare service; these databases are classified as healthcare services, conditions/diseases, and others, on the basis of the recorded observational unit. The authors find that the HUDs are homogeneous with respect to the unique personal identification code, the anonymization technique, and the DMS adopted, so that the record linkage across them is always possible. Additionally, the classification systems for diseases and drugs are found to be homogeneous across regions, while the anonymization procedures are not. This work can be considered as a model for other countries that wish to inventory their available HUDs to ease multicentric epidemiologic studies.

The study of Corrao et al. is a valuable example of real-world evidence that can be extracted from the merging of HUDs [ 10 ]. The authors aim to investigate the association between third-trimester exposure to macrolide antibiotics and the risk of preterm delivery as primary outcome and low birth weight (less than 2500 g), with smallness for gestational age and five-minutes Apgar score <7 as secondary outcomes. The merged HUDs are (1) the electronic database of the NHS beneficiaries, (2) the outpatient drugs dispensed in community pharmacies, and (3) the specialist visits and diagnostic examinations reimbursable by the NHS and the database of the Certificates of Delivery Assistance of all of Lombardy in the period between 2007 and 2017. The merging procedure gives arise to a database of 549,082 mothers with their newborns. From the methodological point of view, this paper contributes for considering the so-called immortal bias, the time window between the index date (27th gestational week) and the start of exposure (first antibiotics prescription) in which the event of interest (preterm birth) is not possible by design.

Finally, the last methodological paper of this special issue is concerned with managing data separation by logistic regression [ 11 ], which is a frequent problem in the analysis of small or sparse clinical datasets. Data can be defined as separated if there exists one covariate or a linear combination of covariates that allows a perfect prediction of some or all observations of the dataset. The authors describe several situations a medical data-analyst is faced with, as the occurrence of unbalanced outcomes [ 15 ], rare exposures, as in the case of case-control studies with controls free of any local and systemic variables [ 16 ], correlated covariates, and sparse data. The authors aim to compare Firth’s penalization, which is widely used to deal with data-separation, with the maximum likelihood method applied to an augmented dataset. Through a well-done data simulation study, they show that bringing more sampling data is not a cost-adjusted relative efficient strategy compared to logistic regression with Firth penalization.

In our Special Issue, quality evaluation of healthcare is addressed from both the patient’s point of view, in terms of quality perception of healthcare services, and from the healthcare point of view, in terms of efficiency and efficacy of care. From the patients’ viewpoint, Druică et al. investigate patients’ health services satisfaction with health services [ 12 ]. On a cross-sectional sample of 1500 Romanian patients, the authors use a partial least square–path modeling approach (PLS–PM) to determine their health services satisfaction. The authors develop a variance-based structural model, emphasizing the mediating role of trust and satisfaction with various health services categories. Results show the mediating role of trust in shaping the relationship between the procedural accuracy of health professionals, the perceived intensity of their interaction with patients, and patients’ experienced quality of the health services. As the most relevant variable for intervention, the authors detect the degree of attention patients perceive to have received. The paper suggests three methods to turn waiting time into attention deserved to patients.

Gálvez et al. evaluate hospitals’ efficiency by comparing a new model of organizational innovation based on Advanced Practice Nurse in the care of people with Ostomies (APN-O) versus usual care [ 13 ]. The study involves twelve Andalusian hospitals that implement this model. On a total of 75 patients followed-up for six months, the authors analyze clinical outcomes, healthcare resources, health-related quality of life, and willingness to pay. The economic evaluation takes into consideration the healthcare direct and indirect cost and finds evidence of an increased value-based healthcare in ostomies. This study suggests that APN-O is an effective and highly efficient patient management model for improving patients’ health status.

Li et al. deal with different objectives in the efficiency evaluation of Chinese hospitals [ 14 ]. In a first step, efficiency and change in efficiency are evaluated using Data Envelopment Analyses and the Malmquist index. The study considers 29 provinces, where data from 1336 hospitals are observed in a time-span from 2003 to 2016; in a second step of the study, starting from the efficiency differences calculated in the first step of the analysis, the authors use the Theil index to obtain the efficiency decomposition. Then, they find the inefficiency determinants through the Grey correlation analysis. The study results show that the township hospitals achieve efficiency gains in most provinces and that the intra-regional difference is the major cause of the overall efficiency scores’ difference. This paper further examines the Grey correlations between overall provincial efficiency difference of Chinese township hospitals and the determinants of such a difference, within each of the eastern, central, and western regions of China.

In conclusion, the authors suggest that local governments should take measures to improve the level of education, increase public financial support for township hospitals, and guide household expenditure to invest more on health care and medical services through public education, so as to shrink the differences among provinces. Furthermore, township hospitals in relatively backward provinces should not ignore the effects of increasing the proportion of licensed doctors and assistant doctors, and the proportion of managerial personnel in the total number of medical personnel.

The Special Issue encompasses different and valuable works related to what extent statistical disciplines are important in the fields of epidemiology, public health, and health promotion. The selected studies for this Special Issue contribute relevant information that may help suggesting appropriate statistical methodologies for specific biomedical research questions and analyzing the quality of services provided in public health. Furthermore, it highlights several statistical methods currently used in public health and epidemiological studies and some of the frequent problems encountered in the analysis of clinical datasets.

Acknowledgments

As Guest Editors of this Special Issue, we would like to acknowledge the contribution of all the authors that participated in this Special Issue, for sharing their expertise and research within this topic. Finally, we are grateful to the editorial staff for the support in the management of this Special Issue.

Author Contributions

D.M., F.B. and L.M. were all responsible for the conceptualization of the paper and for the original draft preparation, as well as for reviewing and editing the final version of the manuscript. All authors have read and agreed to the published version of the manuscript.

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Data availability statement, conflicts of interest.

The authors declare no conflict of interest.

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Search Results

SDMX – statistical data exchange model

Sdmx-ml and sdmx-edi (gesmes/ts): the ecb statistical representation standards.

SDMX-ML is the XML syntax used by the ECB and the national central banks of the EU to disseminate statistics on the Web.

The following sections offer data in SDMX-ML format:

• Selected euro area aggregates and national breakdowns
• ECB Data Portal
• Reference exchange rates - Historical daily data since 1999: SDMX-ML
• Euro area yield curve

SDMX-EDI (GESMES/TS) is the standard used by the ECB to exchange statistical data and metadata with its partners in the European System of Central Banks (ESCB) and other organisations world-wide. It was a key element in the statistical preparations for Monetary Union and has proved both efficient and effective in meeting the ESCB's rapidly evolving statistical requirements. SDMX-ML is now progressively used also in data and metadata exchange.

Both SDMX-EDI and SDMX-ML are part of the SDMX standards. The standards are maintained by the SDMX initiative led by seven international and European organisations (BIS, ECB, Eurostat, IMF, OECD, UN, World Bank).

The International Organization for Standardization (ISO) has approved the SDMX standards as a technical specification ( ISO/TS 17369:2013 ).

• SDMX 2.1 RESTful web services Provides programmatic access to data and metadata disseminated via the ECB Data Portal.
• SDMX tutorial
• www.sdmx.org
• SDMX Roadmap 2020

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National Statistical

News and insight from the office for national statistics, local data at your fingertips.

• Ian Diamond
• March 26, 2024

Local data is a precious commodity that not only allows people to understand more about the area in which they live but helps us identify the challenges and opportunities facing different communities, ultimately enabling better informed policies and decisions that benefit local areas. Sir Ian Diamond explains how the ONS has developed a new service that allows people to find, visualise, compare and download a wide range of these local data in one place.  

Empowering new insights in greater detail   

This time last year I set out our plan for the future of ONS economic statistics to the Royal Society. Enabling the users of statistics to explore our remarkable granular data to conduct their own hyperlocal analyses was, and remains, an important part of that vision.  

The new Explore Local Statistics journey we are launching today is an important service for everyone – from local and central government, devolved administrations, academia, the media, the private sector and all interested citizens seeking detailed insight on research topics. It will help to ensure local leaders and the teams that support them, have access to the data, statistics, and analysis they need to make evidence-based decisions.  

Following on from the launch of ONS Local, it also marks another landmark in the continuing delivery of our strategy for UK public data.  

Knowing where to find it  

While the ONS and other public bodies already gather a lot of local data that can give us all kinds of fascinating insights into different communities and geographic areas, these data aren’t always easy to find or combine, as they are published across many different websites and are often hidden within larger national datasets.   

The situation is further complicated by the complex and evolving landscape of statistical and administrative geographies and by the devolved nature of important policies, which might make it harder for users to find the data for the local area they are interested in.  

Despite these challenges, there is still significant demand for subnational statistics which is why we’ve created the new service.  

Explore local statistics Beta service   

Available through the ONS website, our new Explore local statistics Beta service allows you to see at a glance how a geographical area is doing on a range of indicators, how things have changed over time, and how it compares to other areas or the rest of the country.  

The service also lets you explore local statistics by indicator, giving you the option to delve into one topic across multiple areas, rather than focussing only on one area. Users can even compare their area of choice to other statistically similar areas, as the service incorporates local authority clustering analysis .   

All of this is made possible by bringing together in one place a wealth of publicly available data sources published by ONS, other public bodies or Devolved Administrations.  

Supporting local decision making  

To complement what the service offers to local users, we have also established ONS Local, an analytical advisory service that support s local government across the English regions and the Devolved Administrations in accessing and making the best use of local data , and in identifying and filling local data gaps.  

Both ONS Local and the Explore local statistics service are part of an ambitious three-year project funded by the Department for Levelling Up, Housing and Communities. As part of the project, we are also delivering a suite of new granular statistics, analysis, and place-based insight. Some recent successes of the project, and wider collaborations include: an interactive to explore house and rent prices in your local area ; estimates of gross disposable household income (GDHI) at ‘building block’ level; and analysis on consumer spending habits on a local level .  

Next steps  

In addition to expanding the range of datasets available in the service, we also plan to make it more resilient by further automating the data gathering process.   

I want to take the opportunity to thank our colleagues across ONS, other public bodies, particularly the Department for Levelling Up, Housing and Communities, and Devolved Administrations, for their continued support in delivering towards the Government Statistical Service subnational data strategy . We can expect some exciting developments in the future of subnational statistics!  

As this is a Beta service, thus still in development, your feedback is important to help us improve it, so head over to Explore local statistics and explore it for yourself!    

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Computer Science > Machine Learning

Title: learning using granularity statistical invariants for classification.

Abstract: Learning using statistical invariants (LUSI) is a new learning paradigm, which adopts weak convergence mechanism, and can be applied to a wider range of classification problems. However, the computation cost of invariant matrices in LUSI is high for large-scale datasets during training. To settle this issue, this paper introduces a granularity statistical invariant for LUSI, and develops a new learning paradigm called learning using granularity statistical invariants (LUGSI). LUGSI employs both strong and weak convergence mechanisms, taking a perspective of minimizing expected risk. As far as we know, it is the first time to construct granularity statistical invariants. Compared to LUSI, the introduction of this new statistical invariant brings two advantages. Firstly, it enhances the structural information of the data. Secondly, LUGSI transforms a large invariant matrix into a smaller one by maximizing the distance between classes, achieving feasibility for large-scale datasets classification problems and significantly enhancing the training speed of model operations. Experimental results indicate that LUGSI not only exhibits improved generalization capabilities but also demonstrates faster training speed, particularly for large-scale datasets.

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