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How do I know to reject the null hypothesis?
After you perform a hypothesis test, there are only two possible outcomes.
- When your p-value is less than or equal to your significance level, you reject the null hypothesis. The data favors the alternative hypothesis.
- When your p-value is greater than your significance level, you fail to reject the null hypothesis.
What is the reason of a null hypothesis being rejected?
In the significance testing approach of Ronald Fisher, a null hypothesis is rejected if the observed data is significantly unlikely to have occurred if the null hypothesis were true. In this case, the null hypothesis is rejected and an alternative hypothesis is accepted in its place. If the data is consistent with the null hypothesis statistically possibly true, then the null hypothesis is not rejected.
Why is the null hypothesis often sought to be rejected?
Analysts look to reject the null hypothesis because doing so is a strong conclusion. This requires strong evidence in the form of an observed difference that is too large to be explained solely by chance.
When should a null hypothesis be rejected or accepted?
If our statistical analysis shows that the significance level is below the cut-off value we have set (e.g., either 0.05 or 0.01), we reject the null hypothesis and accept the alternative hypothesis. Alternatively, if the significance level is above the cut-off value, we fail to reject the null hypothesis and cannot accept the alternative hypothesis.
Do I reject or accept the null?
You should never accept the null hypothesis. You should reject it, or fail to reject it. The null hypothesis is is called “null” because it is the “nothing” hypothesis, the result if no new information is gained in the experiment. The null hypothesis is formulated to reflect the current state of knowledge (or currently accepted version of truth).