NepanitaNesg3a

2022-05-02

Show the difference between two approaches to significance testing?

Penelope Carson

Beginner2022-05-03Added 16 answers

The two approaches to significance testing are p-value approach and critical value approach.

In p-value approach, the data gives strong evidence to reject the null hypothesis when it is too small (<0.01). The conclusion of the p-value approach is based on the comparison between p-value of the test and the significance level,$\alpha$ .

Decision:

If p-value is less than or equal to the significance level, the null hypothesis is rejected.

In critical value approach, if the test statistic lies in the critical region, then the null hypothesis is rejected. The conclusion of the critical value approach is based on the comparison between test statistic of the test and the critical value at$\alpha$ of the test .

Decision:

If critical value is greater than the test statistic, the null hypothesis is rejected.

In p-value approach, the data gives strong evidence to reject the null hypothesis when it is too small (<0.01). The conclusion of the p-value approach is based on the comparison between p-value of the test and the significance level,

Decision:

If p-value is less than or equal to the significance level, the null hypothesis is rejected.

In critical value approach, if the test statistic lies in the critical region, then the null hypothesis is rejected. The conclusion of the critical value approach is based on the comparison between test statistic of the test and the critical value at

Decision:

If critical value is greater than the test statistic, the null hypothesis is rejected.

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