Contents

- 1 How do you find the decision rule in statistics?
- 2 What is the decision rule for Chi Square?
- 3 What is the decision rule for t test?
- 4 What does the p-value tell you?
- 5 What is the p-value decision rule?
- 6 What is an example of a decision rule?
- 7 Which of the following is a type I error?
- 8 What would a chi-square significance value of p 0.05 suggest?
- 9 What is a decision rule in statistics?
- 10 What are the steps of chi-square test?
- 11 What is the rejection rule?
- 12 How do you interpret t test results?
- 13 How do you know if the hypothesis is accepted?

## How do you find the decision rule in statistics?

The decision rule is: Reject H_{} if Z > 1.645. The decision rule is: Reject H_{} if Z < 1.645. The decision rule is: Reject H_{} if Z < -1.960 or if Z > 1.960. The complete table of critical values of Z for upper, lower and two-tailed tests can be found in the table of Z values to the right in “Other Resources.”

## What is the decision rule for Chi Square?

If your chi-square calculated value is greater than the chi-square critical value, then you reject your null hypothesis. If your chi-square calculated value is less than the chi-square critical value, then you “fail to reject” your null hypothesis.

## What is the decision rule for t test?

The decision rule, ” reject if |t| > critical value associated with α” is equivalent to “reject if p < α." SAS will provide the p-value, the probability that T is more extreme than observed t. The decision rule, "reject if |t| > critical value associated with α” is equivalent to “reject if p < α."

## What does the p-value tell you?

In statistics, the p-value is the probability of obtaining results at least as extreme as the observed results of a statistical hypothesis test, assuming that the null hypothesis is correct. A smaller p-value means that there is stronger evidence in favor of the alternative hypothesis.

## What is the p-value decision rule?

If the P-value is less than (or equal to), then the null hypothesis is rejected in favor of the alternative hypothesis. And, if the P-value is greater than, then the null hypothesis is not rejected. If the P-value is less than (or equal to), reject the null hypothesis in favor of the alternative hypothesis.

## What is an example of a decision rule?

A decision rule is a simple IF-THEN statement consisting of a condition (also called antecedent) and a prediction. For example: IF it rains today AND if it is April (condition), THEN it will rain tomorrow (prediction).

## Which of the following is a type I error?

A type 1 error is also known as a false positive and occurs when a researcher incorrectly rejects a true null hypothesis. This means that your report that your findings are significant when in fact they have occurred by chance.

## What would a chi-square significance value of p 0.05 suggest?

What would a chi square significance value of P 0.05 suggest *? That means that the p-value is above 0.05 (it is actually 0.065). Since a p-value of 0.65 is greater than the conventionally accepted significance level of 0.05 (i.e. p > 0.05) we fail to reject the null hypothesis.

## What is a decision rule in statistics?

A decision rule spells out the circumstances under which you would reject the null hypothesis. Usually a decision rule will usually list specific values of a test statistic, values which support the alternate hypothesis (the hypothesis you wish to prove or test) and which are contradictory to the null hypothesis.

## What are the steps of chi-square test?

Compare the computed chi-square statistic with the critical value of chi-square; reject the null hypothesis if the chi-square is equal to or larger than the critical value; accept the null hypothesis if the chi-square is less than the critical value.

## What is the rejection rule?

It is a criterion under which a hypothesis tester decides whether a given hypothesis must be accepted or rejected. The general rule of thumb is that if the value of test statics is greater than the critical value then the null hypothesis is rejected in the favor of the alternate hypothesis.

## How do you interpret t test results?

Compare the P-value to the α significance level stated earlier. If it is less than α, reject the null hypothesis. If the result is greater than α, fail to reject the null hypothesis. If you reject the null hypothesis, this implies that your alternative hypothesis is correct, and that the data is significant.

## How do you know if the hypothesis is accepted?

If the tabulated value in hypothesis testing is more than the calculated value, than the null hypothesis is accepted. Otherwise it is rejected. The last step of this approach of hypothesis testing is to make a substantive interpretation. The second approach of hypothesis testing is the probability value approach.