Ace the CRINQ Stats Challenge 2026 – Elevate Your Analytical Game and Conquer Clinical Data!

The key idea is how we decide whether to reject the null hypothesis using a p-value and a pre-set significance level. The significance level (alpha) is chosen before looking at the data and represents the largest probability of a false positive we’re willing to accept. The p-value is the probability, under the null hypothesis, of getting results as extreme as or more extreme than what we observed.

If that p-value falls below the chosen alpha, the observed result is unlikely enough under the null to consider the finding statistically significant, so we reject the null hypothesis. If the p-value is larger than alpha, the data don’t provide enough evidence to reject the null. The p-value is computed from the data, while alpha is set beforehand; they’re compared to make the decision. Often we see the threshold stated as p ≤ alpha, but the core idea is the same: small p-values relative to alpha lead to rejection of the null.