what is alpha in statistics

Alpha in statistics is the significance level: the cutoff you choose before a hypothesis test for how much risk of a false positive you are willing to accept. It is also the probability of a Type I error , meaning you reject a true null hypothesis.

Simple meaning

If you set alpha at 0.05, you are accepting a 5% chance of concluding there is an effect when there really is not. A smaller alpha like 0.01 is stricter, while a larger alpha like 0.10 is more permissive.

How it is used

Researchers compare the p-value to alpha. If the p-value is less than or equal to alpha, they usually reject the null hypothesis; if it is larger, they do not reject it.

Quick example

Suppose a study tests whether a new teaching method works. With alpha = 0.05, the researcher is saying, “I am okay with a 5% chance of a false alarm.”

One-line version

Alpha is the threshold for statistical significance and the risk level you set for making a false positive decision.

TL;DR: In statistics, alpha usually means the significance level, often set at 0.05, and it represents the chance of a Type I error.