# Critical Region, Critical Values and Significance Level

JoVE Core
Statistik
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JoVE Core Statistik
Critical Region, Critical Values and Significance Level

### Nächstes Video9.4: P-value

Hypothesis testing requires the sample statistics—such as proportion, mean, or standard deviation—to be converted into a value or score known as the test statistics.

Assuming that the null hypothesis is true, the test statistic for each sample statistic is calculated using the following equations.

As samples assume a particular distribution, a given test statistic value would fall into a specific area under the curve with some probability.

Such an area, which includes all the values of a test statistic that indicates that the null hypothesis must be rejected, is termed the rejection region or critical region.

The value that separates a critical region from the rest is termed the critical value. The critical values are the z, t, or chi-square values calculated at the desired confidence level.

The probability that the test statistic will fall in the critical region when the null hypothesis is actually true is called the significance level.

In the example of testing the proportion of healthy and scabbed apples, if the sample proportion is 0.9, the hypothesis can be tested as follows.

## Critical Region, Critical Values and Significance Level

The critical region, critical value, and significance level are interdependent concepts crucial in hypothesis testing.

In hypothesis testing, a sample statistic is converted to a test statistic using z, t, or chi-square distribution. A critical region is an area under the curve in  probability distributions demarcated by the critical value. When the test statistic falls in this region, it suggests that the null hypothesis must be rejected. As this region contains all those values of the test statistic (calculated using the sample data) that suggest rejecting the null hypothesis, it is also known as the rejection region or region of rejection. The critical region may fall at the right, left, or both tails of the distribution based on the direction indicated in the alternative hypothesis and the calculated critical value.

A critical value is calculated using the z, t, or chi-square distribution table at a specific significance level. It is a fixed value for the given sample size and the significance level. The critical value creates a demarcation between all those values that suggest rejection of the null hypothesis and all those other values that indicate the opposite. A critical value is  based on a pre-decided significance level.

A significance level or level of significance or statistical significance is defined as the probability that the calculated test statistic will fall in the critical region. In other words, it is a statistical measure that indicates that the evidence for rejecting a true null hypothesis is strong enough. The significance level is indicated by α, and it is commonly 0.05 or 0.01.