Final answer:
Calculating the standard error is not a necessary step in every hypothesis testing procedure. Steps such as formulating the null hypothesis, choosing the significance level, and drawing conclusion based on the p-value are critical parts of hypothesis testing.
Step-by-step explanation:
The step that is not necessary in every hypothesis testing procedure using a test statistic is calculating the standard error (C). This step may not be necessary when the test statistic does not require it, such as when using a test statistic that is based on proportions rather than means, or when the necessary parameters are already provided. Hypothesis testing typically involves the following steps:
- Formulating the null hypothesis (A)
- Choosing the significance level (B)
- Drawing a conclusion based on the p-value and the chosen significance level (D).
Hypothesis testing is a systematic way to evaluate whether sample data provides enough evidence to reject the null hypothesis. Type I error refers to rejecting the null hypothesis when it is actually true, and Type II error refers to failing to reject the null hypothesis when it is false. The level of significance, or alpha (a), is the predetermined probability of making a Type I error.