{"id":218417,"date":"2024-10-20T11:45:49","date_gmt":"2024-10-20T11:45:49","guid":{"rendered":"https:\/\/namso-gen.co\/blog\/when-the-p-value-is-greater-than-the-significance-level\/"},"modified":"2024-10-20T11:45:49","modified_gmt":"2024-10-20T11:45:49","slug":"when-the-p-value-is-greater-than-the-significance-level","status":"publish","type":"post","link":"https:\/\/namso-gen.co\/blog\/when-the-p-value-is-greater-than-the-significance-level\/","title":{"rendered":"When the p-value is greater than the significance level."},"content":{"rendered":"

**When the p-value is greater than the significance level**<\/p>\n

In the world of statistical analysis, the p-value and the significance level play crucial roles in determining the validity of research findings. The p-value represents the probability of obtaining results as extreme as the observed data if the null hypothesis is true. On the other hand, the significance level, often denoted as \u03b1 (alpha), is a predetermined threshold used to determine if the results are statistically significant. Typically, a significance level of 0.05 (or 5%) is commonly used.<\/p>\n

But what happens when the p-value is greater than the significance level? This scenario leads us to reject the null hypothesis, suggesting that the observed data is not statistically significant. Let’s explore this situation further and address some related frequently asked questions.<\/p>\n

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