 # Hypothesis testing and P-values

While evaluating Null and Alternative hypothesis, we tend to use a threshold for a p-value. We usually see the threshold being used as 0.05. Why Is p < 0.05 a Common Standard?

We know that p-value corresponds to the probability that the null hypothesis ( the idea that there is no significant difference between specified populations or study groups ) is true. Basically it is the probability of an observed result arising by chance.

What do we generally do?

If the p-value is less than 0.05, we reject the null hypothesis.
Example : If the p-value is 0.001, it indicates that, if the null hypothesis were indeed true, then there would be only a 1 in 1,000 chance of observing data this extreme. This is an extremely less probability.
The value of 0.05 was selected by Ronald Fisher in the 1920’s. He selected 0.05 partly because of the convenient fact that in a normal distribution, the five percent cutoff falls around the second standard deviation away from the mean. This means we expect that when the null hypothesis is true we will observe values in these regions less than 5% of the times. The arbitrary standard of <5% is considered by convention as sufficiently unlikely, allowing us to reject the null hypothesis.