Do you really understand p-values? They are one of the most widely used and misunderstood tools in statistics. Misunderstanding p-values can lead to incorrect conclusions and poor decisions. Here are five of the most common misconceptions:
1️⃣ A small p-value means the result is important
No. It only suggests the data would be unlikely under the null hypothesis, not that the finding is practically meaningful.
2️⃣ p < 0.05 means the null hypothesis is false
Not necessarily. It means the data are less compatible with the null hypothesis, not that the null is certainly wrong.
3️⃣ p-values tell you the size of the effect
They don’t. A p-value gives no information about effect magnitude. Look at effect sizes and confidence intervals instead.
4️⃣ A p-value works the same no matter how many tests you run
Running many tests increases the chance of obtaining small p-values by chance alone. Without adjusting for multiple comparisons, you increase your risk of false positives.
5️⃣ A p-value is meaningful without checking assumptions
If the assumptions of the statistical test are violated, the p-value may not be valid, even if it is small.
A p-value is the probability of obtaining results at least as extreme as those observed, assuming the null hypothesis and all test assumptions are true. It does not tell you whether the null hypothesis is true or false, nor does it measure the size or importance of an effect. It is one piece of evidence to help assess how compatible your data are with the null hypothesis.
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