The Importance of Test Statistic in Hypothesis Testing | A Crucial Component in Making Informed Decisions about Population Parameters

The value calculated from the sample which is used to perform the test

The value calculated from a sample that is used to perform a test is typically referred to as a test statistic

The value calculated from a sample that is used to perform a test is typically referred to as a test statistic. In hypothesis testing, the test statistic is a numerical summary of the sample data that allows us to determine whether or not there is enough evidence to support a particular claim or hypothesis about a population parameter.

The choice of the test statistic depends on the specific hypothesis being tested and the type of data being analyzed. It is typically chosen so that it captures any relevant information or patterns in the sample data that are indicative of the hypothesis being tested.

Once the test statistic is calculated, it is compared to a critical value or a p-value to make a decision about the hypothesis. The critical value is a cutoff point beyond which we reject the null hypothesis, while the p-value is the probability of observing a test statistic as extreme or more extreme than the one calculated, assuming the null hypothesis is true.

If the calculated test statistic falls in the critical region (beyond the critical value) or has a p-value that is smaller than the predetermined level of significance, we reject the null hypothesis in favor of the alternative hypothesis. If the test statistic does not fall in the critical region or has a p-value greater than the significance level, we fail to reject the null hypothesis.

In summary, the value calculated from a sample, known as the test statistic, is a crucial component in hypothesis testing and helps us make informed decisions about population parameters based on sample data.

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