hypothesis testing krish naik

• Last updated October 7, 2021
• In Intellectual AI Discussion

Krish Naik Speaks About His ML Journey & Advice To Data Scientists

• Published on July 15, 2021
• by kumar Gandharv

Krish Naik is a hot shot in the field of data science education with over 397k subscribers for his YouTube channel. He is the co-founder of iNeuron.ai, where he dons both CIO and CMO hats.

Analytics India Magazine got in touch with Krish Naik to understand his ML Journey and his take on India’s data science landscape .

AIM: What attracted you to the field of data science?

Krish: Before starting in data science and AI, I was a software engineer moving between net programming and Java. During that time, I had never heard about AI. In one of the projects, we were implementing a module based on personalisation using net programming. The functionality was very simple where based on a login, we had to recommend various insurance products to the user. 

Once the project got implemented, we got special recognition for this module, and the team called it an AI module. That was the first time when I first got to know about AI and machine learning. This excellent use case ignited a fire in me to learn about machine learning and data science. I started exploring more about it and could see how many different use cases and problems it could solve with ease. When I started working on some of the projects, I could see the overall development in me in terms of business, technical and presentation and many more things. Apart from this, I am also a huge fan of Mathematics and Stats, which motivated me to move towards data science. 

So until now, I have worked in more than four companies where I have successfully implemented around 6-7 projects in the field of data science and helped them earn exceptional revenues. I have also co-founded a company named iNeuron Intelligence (ineuron.ai), where we focus on providing affordable courses on AI-related technologies and parallelly, we do AI product development.  

AIM: What’s your advice for aspirants embarking on an ML journey?

Krish: If anybody wants to start their ML journey now, you need to focus on three perspectives:

• Business Problem (domain knowledge)
• Technical skills
• Outcomes and Improvements of the solution

Business problem: As a data scientist, we need to think about the business problem and what use case we are solving. Not every issue needs to be solved by using data science or machine learning. To start learning, we need to focus on understanding the domain because that is the best step from where your requirement gathering and data gathering processes will start. The more you understand the business and the domain, the better solution you will be able to create with better accuracy.

Technical skills: Technical skills involve understanding the programming language (I would suggest going with Python), understanding the entire lifecycle of data science projects, model deployment and retraining approaches.

Outcomes and improvements: This step involves building the product with better functionalities and modules, bringing more improvements in the products. These steps are always to be kept in mind since the industries will be working in this manner, and if you have this thought process, it will definitely help you.

AIM: What tools do you use?

Krish: From a language perspective, I use Python, javascript, .net., frameworks and related libraries. I also use a lot of GPU computing.

NVIDIA RAPIDS is one of the best tools for accelerated data science, especially when I want to do hyperparameter optimization. Unfortunately, CPUs are quite slow, so NVIDIA Cuda Libraries come to the rescue of NVIDIA Titan RTX. The NVIDIA GeForce RTX 3060 is the latest NVIDIA GPU I have been using.

All in all, I can say that this acceleration helps to complete my everyday data science tasks faster, and I spend more time in algorithm optimization and insights gathering rather than spending time waiting for pre-processing and model training to get completed.

AIM: What are your thoughts on India’s AI/ML landscape?

Krish: I have been working in the industry for about five years, and the Indian AI and Analytics startups continued to attract investment in 2019, receiving $762.5 million in funding, a 44% increase over the $530 million funding received in 2018. This steady growth in funding seems insignificant compared with the 368% growth from 2017 and 2018. This continued growth has attracted many people to move into this sector. This sector has also created many jobs, and many people are able to get some amazing salaries.

AIM: You have said 2021 is the year of hiring data scientists. Could you elaborate? 

Krish: According to the recent survey from Gartner, around 4-5% have started incorporating AI in their day to day activities, and they also have mentioned that it’s going to increase in the upcoming five years. Apart from this, in the past six months in 2021, I have seen more than 500+ of my subscribers and students from iNeuron from different domains making a successful transition to data science. And more and more requirements are coming up in companies for these specific roles in Analytics . So this gives a hint that this year will be amazing for different data scientist roles.

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Hypothesis Testing | A Step-by-Step Guide with Easy Examples

Published on November 8, 2019 by Rebecca Bevans . Revised on June 22, 2023.

Hypothesis testing is a formal procedure for investigating our ideas about the world using statistics . It is most often used by scientists to test specific predictions, called hypotheses, that arise from theories.

There are 5 main steps in hypothesis testing:

• State your research hypothesis as a null hypothesis and alternate hypothesis (H o ) and (H a  or H 1 ).
• Collect data in a way designed to test the hypothesis.
• Perform an appropriate statistical test .
• Decide whether to reject or fail to reject your null hypothesis.
• Present the findings in your results and discussion section.

Though the specific details might vary, the procedure you will use when testing a hypothesis will always follow some version of these steps.

Table of contents

Step 1: state your null and alternate hypothesis, step 2: collect data, step 3: perform a statistical test, step 4: decide whether to reject or fail to reject your null hypothesis, step 5: present your findings, other interesting articles, frequently asked questions about hypothesis testing.

After developing your initial research hypothesis (the prediction that you want to investigate), it is important to restate it as a null (H o ) and alternate (H a ) hypothesis so that you can test it mathematically.

The alternate hypothesis is usually your initial hypothesis that predicts a relationship between variables. The null hypothesis is a prediction of no relationship between the variables you are interested in.

• H 0 : Men are, on average, not taller than women. H a : Men are, on average, taller than women.

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For a statistical test to be valid , it is important to perform sampling and collect data in a way that is designed to test your hypothesis. If your data are not representative, then you cannot make statistical inferences about the population you are interested in.

There are a variety of statistical tests available, but they are all based on the comparison of within-group variance (how spread out the data is within a category) versus between-group variance (how different the categories are from one another).

If the between-group variance is large enough that there is little or no overlap between groups, then your statistical test will reflect that by showing a low p -value . This means it is unlikely that the differences between these groups came about by chance.

Alternatively, if there is high within-group variance and low between-group variance, then your statistical test will reflect that with a high p -value. This means it is likely that any difference you measure between groups is due to chance.

Your choice of statistical test will be based on the type of variables and the level of measurement of your collected data .

• an estimate of the difference in average height between the two groups.
• a p -value showing how likely you are to see this difference if the null hypothesis of no difference is true.

Based on the outcome of your statistical test, you will have to decide whether to reject or fail to reject your null hypothesis.

In most cases you will use the p -value generated by your statistical test to guide your decision. And in most cases, your predetermined level of significance for rejecting the null hypothesis will be 0.05 – that is, when there is a less than 5% chance that you would see these results if the null hypothesis were true.

In some cases, researchers choose a more conservative level of significance, such as 0.01 (1%). This minimizes the risk of incorrectly rejecting the null hypothesis ( Type I error ).

The results of hypothesis testing will be presented in the results and discussion sections of your research paper , dissertation or thesis .

In the results section you should give a brief summary of the data and a summary of the results of your statistical test (for example, the estimated difference between group means and associated p -value). In the discussion , you can discuss whether your initial hypothesis was supported by your results or not.

In the formal language of hypothesis testing, we talk about rejecting or failing to reject the null hypothesis. You will probably be asked to do this in your statistics assignments.

However, when presenting research results in academic papers we rarely talk this way. Instead, we go back to our alternate hypothesis (in this case, the hypothesis that men are on average taller than women) and state whether the result of our test did or did not support the alternate hypothesis.

If your null hypothesis was rejected, this result is interpreted as “supported the alternate hypothesis.”

These are superficial differences; you can see that they mean the same thing.

You might notice that we don’t say that we reject or fail to reject the alternate hypothesis . This is because hypothesis testing is not designed to prove or disprove anything. It is only designed to test whether a pattern we measure could have arisen spuriously, or by chance.

If we reject the null hypothesis based on our research (i.e., we find that it is unlikely that the pattern arose by chance), then we can say our test lends support to our hypothesis . But if the pattern does not pass our decision rule, meaning that it could have arisen by chance, then we say the test is inconsistent with our hypothesis .

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

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

Hypothesis testing is a formal procedure for investigating our ideas about the world using statistics. It is used by scientists to test specific predictions, called hypotheses , by calculating how likely it is that a pattern or relationship between variables could have arisen by chance.

A hypothesis states your predictions about what your research will find. It is a tentative answer to your research question that has not yet been tested. For some research projects, you might have to write several hypotheses that address different aspects of your research question.

A hypothesis is not just a guess — it should be based on existing theories and knowledge. It also has to be testable, which means you can support or refute it through scientific research methods (such as experiments, observations and statistical analysis of data).

Null and alternative hypotheses are used in statistical hypothesis testing . The null hypothesis of a test always predicts no effect or no relationship between variables, while the alternative hypothesis states your research prediction of an effect or relationship.

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Bevans, R. (2023, June 22). Hypothesis Testing | A Step-by-Step Guide with Easy Examples. Scribbr. Retrieved April 12, 2024, from https://www.scribbr.com/statistics/hypothesis-testing/

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Kalyanm45 / complete-data-science-materials.

This is Krish Naik Sir's GitHub repository, which I forked. It contains comprehensive resources and materials for data science, including his YouTube video tutorials and open-source contributions.

• Updated Feb 6, 2024

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