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Can a rejected null hypothesis be true?
If there is less than a 5% chance of a result as extreme as the sample result if the null hypothesis were true, then the null hypothesis is rejected. When this happens, the result is said to be statistically significant .
What is rejecting the null when it is true?
Rejecting the null hypothesis when it is in fact true is called a Type I error. When a hypothesis test results in a p-value that is less than the significance level, the result of the hypothesis test is called statistically significant.
What type of error occurs when the null hypothesis is not rejected when it should be rejected?
Type II error is the error made when the null hypothesis is not rejected when in fact the alternative hypothesis is true. The probability of rejecting false 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.
Does failing to reject null mean null is true?
In a similar way, a failure to reject the null hypothesis in a significance test does not mean that the null hypothesis is true. It only means that the scientist was unable to provide enough evidence for the alternative hypothesis.
How low does a p-value have to be to reject a null hypothesis?
The level of statistical significance is often expressed as a p -value between 0 and 1. The smaller the p-value, the stronger the evidence that you should reject the null hypothesis. A p -value less than 0.05 (typically ≤ 0.05) is statistically significant.
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.