# Often asked: How To Make A Decision Rule In Statustics?

## 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 is the decision rule for the p-value approach?

If the P – value is less than (or equal to), reject the null hypothesis in favor of the alternative hypothesis. If the P – value is greater than, do not reject the null hypothesis.

## What would be an appropriate decision rule?

The decision rule would be: if the observed data is in the rejection region, then reject H0. If not, we fail to reject H0. Note that it is not always the best way that we choose most extreme value as the cutoff value. Or else the world is simple.

You might be interested:  Readers ask: Did You Ever Make A Risky Decision Why How Did You Handle It?

## 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 is p-value formula?

The p-value is calculated using the sampling distribution of the test statistic under the null hypothesis, the sample data, and the type of test being done (lower-tailed test, upper-tailed test, or two-sided test). The p-value for: an upper-tailed test is specified by: p-value = P(TS ts | H is true) = 1 – cdf(ts)

## What is disjunctive decision rule?

The disjunctive decision rule establishes a minimum level of performance for each important attribute (often a fairly high level). All brands that surpass the performance level for any key attribute are considered acceptable.

## What is p-value in hypothesis testing?

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.

## How do you find the p-value in a hypothesis test?

If your test statistic is positive, first find the probability that Z is greater than your test statistic (look up your test statistic on the Z-table, find its corresponding probability, and subtract it from one). Then double this result to get the p-value.

## How do you know when to reject the null hypothesis p-value?

A p-value less than 0.05 (typically ≤ 0.05) is statistically significant. It indicates strong evidence against the null hypothesis, as there is less than a 5% probability the null is correct (and the results are random). Therefore, we reject the null hypothesis, and accept the alternative hypothesis.

You might be interested:  Question: Sentences On How You Make A Good Decision?

## What is the rejection rule?

A rejection rule is a logical condition or a restriction to the value of a data item or a data group which must not be met if the data is to be considered correct. In various connections other terms are used, e.g. Y-rule.

## What is the economic decision rule?

Economic decision rule. A rule in economics asserting that if the marginal benefit of an action is higher than the marginal cost, then one should undertake the action; however if the marginal cost is higher than the marginal benefit of the action, one should not undertake it.

## 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.

## 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.

## What is the paired t test?

The paired t-test is a method used to test whether the mean difference between pairs of measurements is zero or not.