What is a p value?

What is a p value?


The p-value provides an estimate of how often we would get the obtained result by chance if in fact, the null hypothesis is true.

In statistics a result is called statistically significant if it's unlikely to have occurred by chance alone.

The most commonly used standard or cutoff is 0.05 or 5%. Because this standard, or cutoff is so important it has a special name.

It's called the significance level of a test, and is usually denoted by the Greek letter alpha, so alpha equals 0.05. 

If the p-value is small, less than 0.05, this suggests that it is more than 95% likely that the association of interest would be present following repeated samples drawn from the population. AKA, a sampling distribution.

If the p-value is less than alpha, which is usually 0.05, then the data we got is considered to be rare or surprising enough when the null hypothesis, H0 is true.

And we say, that the data provides significant evidence against the null hypothesis. So, we reject the null hypothesis and accept the alternate hypothesis, Ha. If the p-value is greater than alpha, then the data is not considered to be surprising enough when the null hypothesis is true. And we say, that our data do not provide enough evidence to reject the null hypothesis. Or equivalently, that the data do not provide enough evidence to accept the alternate hypothesis.

This p-value is also known as the Type One Error Rate, since it denotes the number of times we would be wrong to reject the null hypothesis when it was true.


Rejecting the null hypothesis when it's true is also called a Type One Error.

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