February 22, 2022

Hypothesis Testing

Hypothesis testing is one of the basic inference method used in statistics and this post is intended to cover what hypothesis testing is on a high level, terms used, definitions and can be brief refresher . It is by no means a deep dive and reference section has some good links if you want a detailed study.

What is hypothesis testing ?

It is a part of statistical analysis, where we test the assumption made regarding a population parameter.  It is generally used when we want to compare a single group with an external standard or  two/more groups with each other.


Statistical Significance - It is the probability of how unlikely the outcome have been if it just happened by random choice.

Null Hypothesis - It is a statistical theory that suggests there is no statistical significance exists between the populations. It states that there is no relationship or no effect. It is denoted by H0 and read as H-naught.

Alternative Hypothesis - It suggests there is a significant difference between the population parameters. It could be greater or smaller, it is the contrast of Null hypothesis. It is denoted by Ha.

Level of Significance (alpha) - It is the probability of rejecting the null hypothesis when it is true.

P- Value - It is the probability that random chance generated the data or something else that is equal or rarer, assuming the truth of null hypothesis. It tells how likely it is that your data could fall under or closer to the null hypothesis.


Types of Hypothesis Testing

Cheat Sheet

Different kinds of Hypothesis Testing with a cheat sheat is below.