By Learn Statistics Easily
The probability of observing your data, or more extreme, if the null hypothesis is true.
Rooted in 20th-century statistics, p-values quantify evidence against a null hypothesis.
P-values don't measure the probability that the studied hypothesis is true.
Traditionally, p < 0.05 is considered statistically significant, but it's not a magic number.
Misusing p-values can lead to false conclusions about the evidence.
Interpret p-values within your study's context, considering other statistical measures.
Consider confidence intervals and effect sizes to get a fuller picture.
Replicating studies strengthens the reliability of p-value interpretations.
Apply p-values wisely in hypothesis testing, avoiding overreliance.
The scientific community is revisiting how we use and interpret p-values.