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The critical value essentially is: if you get some statistic in your sample that is so different from the average, it is likely a real pattern, and not simply an artifact of your sample being unusual.
The critical value for a particular test (i.e., for a particular statistical hypothesis and alpha value) is the value of the test statistic such that if the observed test statistic is more extreme, the statistical hypothesis is rejected.
The critical value defines the critical (rejection) region. If you use a z-statistic for a 2-tail test with alpha=0.05 then you get two critical values: z=-1.96 and z=+1.96. If you do the test and z_observed=2.2, you reject the null H and accept the alternative H. The p-value = P (z<-2.2)+P (z>+2.2) assuming H0 is true: p-value=0.028.
Critical values (CV) are the boundary between nonsignificant and significant results in hypothesis testing. Test statistics that exceed a critical value have a low probability of occurring if the null hypothesis is true.
It is used to evaluate whether to reject the null hypothesis in statistical tests or to define the range of values that would include a specified percentage of data in confidence intervals. The critical value is essential in interpreting p-values and establishing significance levels.
A critical value is a threshold that separates the region where the null hypothesis is accepted from the region where it is rejected in hypothesis testing. This value is determined based on the significance level and the distribution of the test statistic.
A critical value is the point (or points) on the scale of the test statistic beyond which we reject the null hypothesis, and is derived from the level of significance $\alpha$ of the test. You may be used to doing hypothesis tests like this: Calculate test statistics. Calculate p-value of test statistic.
