How to interpret a p-value?

Welcome! Looking forward to our forthcoming interaction.

Thanks very much.

When interpreting a p-value, it provides information regarding the strength of evidence against the null hypothesis in a hypothesis test. Here's a general guideline for interpreting p-values:
1. Compare the p-value to the significance level (α): The significance level, often denoted as α, is a predetermined threshold set by the researcher to determine the level of evidence required to reject the null hypothesis. Commonly used significance levels are 0.05 (5%) or 0.01 (1%). If the p-value is less than or equal to the chosen significance level, it is considered statistically significant, suggesting that the evidence is strong enough to reject the null hypothesis. If the p-value is greater than the significance level, it is not statistically significant, and there is not enough evidence to reject the null hypothesis.
2. Small p-value: A small p-value (e.g., p ≤ α) indicates that the observed data are unlikely to occur under the assumption of the null hypothesis. It suggests that there is strong evidence against the null hypothesis and supports the alternative hypothesis. In this case, you may conclude that the effect or relationship being tested is statistically significant.
3. Large p-value: A large p-value (e.g., p > α) suggests that the observed data are likely to occur by chance alone, even if the null hypothesis is true. It indicates that there is not enough evidence to reject the null hypothesis. In this case, you may conclude that the effect or relationship being tested is not statistically significant.
4. Caution with p > α: It's important to note that failing to reject the null hypothesis (when p > α) does not prove that the null hypothesis is true. It only implies that there is not enough evidence to suggest otherwise based on the data analyzed. Additionally, the p-value does not provide information about the magnitude or practical significance of the effect or relationship being tested.
5. Consider other factors: When interpreting p-values, it's crucial to consider the context, study design, sample size, effect size, and practical implications of the research question. A statistically significant result may have limited practical significance, while a non-significant result may still have important implications in certain contexts.