Clearly explained with examples When I was beginning my journey in Data Science many many years ago, I used to think probability and likelihood are the same concepts. But they are not. It is very important to understand the difference to clearly grasp many concepts in Statistics.

Standard deviation and standard error are very important statistical concepts that often cause confusion. Both standard deviation and standard error measure the variation in some data points. But, what is the difference? Standard deviation measures the variation of the observations in a sample across its sample mean. …

All you need to know about the accuracy paradox and how to avoid it When you have highly imbalanced data, the accuracy paradox tells why it is a bad idea to measure the model’s predictive performance using accuracy. This paradox shows us that higher accuracy does not necessarily result in…

What is the confidence interval? Why do we need that? In 2020, the Economist predicted that Joe Biden is like to win the presidential election with 56.4% of the votes compared to 44.6% votes for Donald Trump with a 95% confidence interval of ± 3.4. What does 95% mean here? …

The Core Idea The mean and standard deviation of a sample comes closer to the mean and the standard deviation of the population as the size of the sample increases. This is also known as Bernoulli’s Theorem. Example Let’s understand this concept through an example. Let’s say we have a…

One of the most influential concepts in statistics, if not in the whole of Mathematics What is the core idea behind CTL? Take any random variable — The age of the people in a country, the amount of return you get from a basket of stocks (e.g. S&P 500), the…

Why do we actually need sampling distributions in practice? How can we find the mean age of people living in the USA? Find out the age of each and every person living in the USA and take the average. Simple, isn’t it? But there is one problem. It would be…

How to create tables using SQL? Up until now, we have been using the tables we had already created. Today, we are going to change that and do something new. We are going to create our own tables! So far, we have explored the following: Lesson 1 — SELECT command …

I am sure you must have learned the following definitions of type I and type II errors in your first stat course: Type I error (α , also called significance level) is the probability of rejecting the null hypothesis (H₀) when it is true. Type II error (β) is the…