What is a p-value?
A p-value is the probability of seeing a result at least as extreme as yours if there were actually no real difference. A small p-value means data like yours would be unusual if there were no real difference.
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How to read a p-value
Suppose an A/B test shows variant B converting better, with a p-value of 0.03. That means that if the two versions truly performed the same, you'd see a gap this large or larger only about 3% of the time.
Teams usually pick a cutoff in advance, often 0.05, and call results below it statistically significant.
Common p-value mistakes
A p-value isn't the probability that your result is wrong or that your hypothesis is true. It also says nothing about how large or valuable the effect is, so read it with the effect size and confidence interval.
Checking results repeatedly and stopping the moment p dips below your cutoff inflates false positives. Decide the sample size or test length in advance.
Common questions
What is a good p-value?
Many teams use 0.05 as a cutoff, so a p-value below it counts as significant. The right threshold depends on how costly a wrong call would be.
What does p less than 0.05 mean?
It means that if there were no real effect, results this extreme would occur less than 5% of the time. It doesn't guarantee the effect is real or important.
Can a p-value be too high to trust a result?
A high p-value means the data doesn't clearly rule out chance. It doesn't prove there's no effect, because the test may simply have too little data.
What is the difference between a p-value and a confidence interval?
A p-value measures how compatible the data is with no effect. A confidence interval gives a range of plausible effect sizes.
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