Understanding the Significance of P-Values in Statistical Analysis: When to Reject or Retain the Null Hypothesis

A low p value = reject the null hypothesis/keep the null hypothesis

When analyzing data using statistical tests, the p-value is a measure of the evidence against the null hypothesis

When analyzing data using statistical tests, the p-value is a measure of the evidence against the null hypothesis. The null hypothesis represents the statement of no effect or no difference between groups or variables being compared.

If the p-value is low, typically set at a significance level of 0.05 (5%), it means that the observed data is unlikely to occur if the null hypothesis were true. In this case, it suggests strong evidence against the null hypothesis and indicates that there is a significant effect or difference present.

Therefore, if the p-value is low, typically less than 0.05, it is generally considered appropriate to reject the null hypothesis. Rejecting the null hypothesis means accepting that there is a significant relationship or difference between variables or groups being compared.

However, if the p-value is not low (i.e., greater than or equal to 0.05), it suggests weak or insufficient evidence against the null hypothesis. In such cases, it is appropriate to retain or fail to reject the null hypothesis, indicating that there is not enough evidence to support a significant effect or difference.

Remember, a low p-value signifies stronger evidence against the null hypothesis, while a high p-value suggests weaker evidence and leads to the decision of keeping or retaining the null hypothesis.

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