**P-value** helps us determine how likely it is to get a particular result when the null hypothesis is assumed to be true. It is the probability of getting a sample like ours or more extreme than ours if the null hypothesis is correct. Therefore, if the null hypothesis is assumed to be true, the p-value gives us an estimate of how “strange” our sample is.

If the p-value is very small (<0.05 is considered generally), then our sample is “strange,” and this means that our assumption that the null hypothesis is correct is most likely to be false. Thus, we reject it. Let us understand what is p-value through few examples:

**Tossing a Coin –**

There are two possible outcomes – Heads (H), Tails (T). Let the null hypothesis be H0, and the alternative hypothesis be H1. H0: This is a fair coin; H1 This is a biased or an unfair coin. Let us assume that we are in a universe where the null hypothesis is true. Consider the following events –

```
Event p-value
0.5 T
0.25 T
0.12 T
0.06 T
0.03 T
0.01 T
```

**p-value** is not –

The probability that the claim is valid.

The probability that the null hypothesis is true.

It is the parameter that helps us determine how “strange” the sample is under the assumption that the null hypothesis is correct. Thereby, it helps us to modify the null hypothesis accordingly