Understanding to p-value and hypothesis testing

Data Science, AI and ML
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#1

What are p-value and hypothesis testing?

#2

The Hypothesis Testing is a statistical test used to determine whether the assumption assumed for the sample of data stands true for the entire population or not.

A p-value is used in hypothesis testing to help you support or reject the null hypothesis.

#3

In the world of statistics, the p-value is NOT the probability of the null being true or not true, but is rather a way of saying how emphatically to reject the null hypothesis.

p-values can be interpreted in other ways:

  • If used in hypothesis testing, p is the probability of your value coming from the hypothetical (Ho) distribution, which is why one rejects Ho if the p value is small.
  • If used in confidence intervals, it’s the probability of a random sample from the same distribution to not be in a certain interval. So you may come across papers which list statistics like 30 +/- 5% (p-value 0.01) meaning that samples from said population shall be 25 to 35 with 99% of probability.