# P-Value in Hypothesis Testing

P-Value is quite confusing and often misinterpret. Let’s clear that up once and for all.

While performing a Hypothesis Testing, we set a significance value, let’s say 0.05. But how do we confirm if we can safely reject the null hypothesis? That is defined by the P-value of the experiment.

P values evaluate how well the sample data supports that the null hypothesis is true. It measures how correct your sample data are with the null hypothesis.

A low P value suggests that your sample provides enough evidence that you can reject the null hypothesis for the entire population. If you got a P-Value of anything less than 0.05 in our case, then you can safely say that the null hypothesis can be rejected. In other words, the sample you took from the population didn’t occur by pure chance and the experiment indeed had a significant effect. Still 95% sure, though.

But how is this P-Value calculated? The answer is, Statistical Tests. There are different types of Statistical Tests for different types of problems.

#statistics #datascience #machinelearning #ml