A

The

In general, the decision rule is that:

- If the magnitude of the calculated test statistic
*exceeds*the rejection point(s), the result is considered*statistically significant*and the null hypothesis (H_{0}) should be rejected. - Otherwise, the result is considered
*not statistically significant*and the null hypothesis (H_{0}) should*not be rejected*.

The specific decision rule varies depending on two factors:

- The distribution of the test statistic.
- Whether the hypothesis test is one-tailed or two-tailed.

For the following discussion, you assume that the test statistic follows a standard normal distribution. Therefore:

- The calculated value of the test statistic is a standard normal variable, denoted by z.
- Rejection points are determined from the standard normal distribution (z-distribution).

A two-tailed hypothesis test for the population mean (μ) is structured as H

where z

For a two-sided test at the 5% significance level, the total probability of a Type I error must sum to 5%, with 5%/2 = 2.5% in each tail. As a result, the two rejection points are -z

A one-tailed hypothesis test for the population mean (μ) is structured as H

where z

For a test of H

If a one-tailed hypothesis test for the population mean (μ) is structured as H

Making a statistical decision involves using the stated decision rule to determine whether or not to reject the null hypothesis. A

For example, it may be that a particular strategy has been shown to be statistically significant in generating value-added returns. However, the costs of implementing that strategy may be such that the added value is not sufficient to justify the costs required. Thus, while statistical significance may suggest a particular course of action is optimal, the economic decision also takes into account the costs associated with the strategy, and whether the expected benefits justify implementing the strategy.

Researchers frequently report the p-value associated with a particular test in order to present a fuller picture.

If you go back to subject a, you will see that seven steps in any hypothesis testing procedure are listed. These seven steps compose the process that needs to be carried out in order to perform a test.

However, there is an alternative and widely used approach to hypothesis testing, which will be discussed here. This is not a different method, but rather a different way of presenting the results of a test. The new approach is known as the p-value approach to hypothesis testing.

Before proceeding with the new approach, a summary of the seven steps encountered earlier:

Step 1: State the hypotheses.

Step 2: Identify the test statistic and its probability distribution.

Step 3: Specify the significance level.

Step 4: State the decision rule.

Step 5: Collect the data in the sample and calculate the necessary value(s) using the sample data.

Step 6: Make a decision regarding the hypotheses.

Step 7: Make a decision based on the test results.

When a p-value approach is used, steps 3 and 4 become redundant. The new steps are as follows:

Step 1: State the hypotheses.

Step 2: Identify the test statistic and its probability distribution.

Step 3: Collect the data in the sample and calculate the necessary value(s) using the sample data.

Step 4: Calculate the p-value for the test.

Step 5: Make a decision regarding the hypotheses.

Step 6: Make a decision based on the test results.

It can thus be seen that the p-value approach is simpler, and also provides useful information about a test statistic. So, by now you are probably anxious to find out: What exactly is a p-value?

The term "tail of interest" means the right-hand tail in a > test, the left-hand tail in a < test, and both tails in a ≠ test.

It should be clear to you that if the test statistic is very extreme (that is, close to the tail) then the area from that value to the end of the tail will be very small. By definition, the p-value is thus very small. In this case, the test statistic would certainly fall in the rejection region.

Conversely, if the test statistic is not extreme (that is, it is far from the tail) and would thus fall in the acceptance region, the area from the test statistic to the end of the tail would be fairly large. In this case, the p-value is fairly large.

So, you have just learned how to interpret p-values. A small p-value indicates a rejection of the null hypothesis whereas a large p-value indicates a non-rejection of the null hypothesis.

This is all well and good, but how large is large?

P-value is normally compared with our usual α value, so 0.05 is a common reference point. This means that, for p-values smaller than 0.05, H

However, p-value provides not only a reference point but an idea about just how strong or weak this rejection or non-rejection is.

For example, a p-value of 0.0003 would indicate a very strong rejection of H

In general, the larger the p-value, the smaller the likelihood that H

In the case of a two-sided test, if the area from the test statistic to the end of the right tail is 0.04, for example, then the p-value will be 0.08; doubling the area is necessary because both tails are being used. It's as simple as that.

Now try the following for yourself. In each case, determine whether or not you would reject H

You are given the following p-values.

A. 0.02

B. 0.456

C. 0.053

D. 0.0001

The correct answers are:

A. Reject H

B. Do not reject H

C. Do not reject H

D. Reject H

Now that you understand how p-values work, let us go through the testing procedure again:

- First write down your hypotheses, then determine which test statistic to use. There is no need to have a significance level, and hence, you don't have a specifically demarcated rejection region.
- Calculate the test statistic and determine the resultant p-value.
- The conclusion of the test is based on your p-value. Recall that a small p-value means a rejection of H
_{0}, whereas a large p-value means a non-rejection of H_{0}.

Note: P-values can be worked out for all continuous statistical distributions, no matter what the shape of the distribution is. So, this method can be used for z-graphs, t-graphs, x

gill15: Notes here need to be condensed to p value less then z ---- reject null |

sharky7: *Decision rule |

bschmitt: it is written here that the p-value is not required by the study guide, but I find it in the study guide and I do not see it would not be required. can anyone help on that please? |

mcbreatz: The P value is about 3 paragraphs in the text and it is pretty much explained exactly as above where smaller than critical value will reject null. Might possibly be 1 question on test. |

: teste |