Rejecting the null hypothesis is the researcher’s conclusion that the observed data are sufficiently inconsistent with the null hypothesis, suggesting that your alternative hypothesis provides a better explanation for the results. A statistically significant result where you reject the null hypothesis can be driven by a tiny effect that is irrelevant in practice, while a large effect that is not statistically significant may indicate the study lacked power.
Simple Guide to Interpreting the Reject Null Hypothesis Conclusion
Transparent reporting of methods, assumptions, and any deviations allows readers to assess whether the decision to reject the null hypothesis is justified and whether the findings can be generalized to the target population. Random assignment, adequate sample size, reliable measurements, and appropriate control of confounding variables reduce bias and increase confidence in the results.
Fail to reject the null hypothesis Insufficient evidence to conclude an effect or difference, not proof of no effect. A test statistic is calculated from the sample, and its associated p-value indicates the probability of observing data at least as extreme as yours if the null hypothesis were true.
Simple Guide to Interpreting the Reject Null Hypothesis Conclusion
Statistical hypothesis testing is a method for using sample data to evaluate this claim and decide whether the evidence is strong enough to overturn the default assumption of no effect. 05, you conclude that the data are unlikely under the null and you reject the null hypothesis in favor of the alternative.
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