How do you determine if a result is statistically significant?

To determine if a result is statistically significant, you compare the p-value derived from your statistical test to a predetermined significance level, often denoted as alpha (α), typically set at 0.05 or 0.01. The p-value indicates the probability of observing the data, or something more extreme, if the null hypothesis is true. If the p-value is less than alpha, it suggests that the observed effect is unlikely to have occurred by chance alone, leading researchers to reject the null hypothesis and conclude that the result is statistically significant.

In practical terms, a p-value lower than the significance level indicates strong evidence against the null hypothesis, while a higher p-value suggests insufficient evidence to assert that an effect exists. Therefore, identifying the relationship between p-value and alpha is a critical step in hypothesis testing and draws a clear boundary for significance in research findings.