chapter 9 hypothesis testing ii. chapter outline introduction hypothesis testing with sample means...

Chapter 9

Hypothesis Testing II

Chapter Outline

Introduction Hypothesis Testing with Sample

Means (Large Samples) Hypothesis Testing with Sample

Means (Small Samples) Hypothesis Testing with Sample

Proportions (Large Samples)

Chapter Outline

The Limitations of Hypothesis Testing: Significance Versus Importance

Interpreting Statistics: Are There Significant Differences in Income Between Men and Women?

In This Presentation

The basic logic of the two sample case.

Test of significance for sample means (large samples).

The difference between “statistical significance” and “importance”.

Basic Logic

We begin with a difference between sample statistics (means or proportions).

The question we test: “Is the difference between statistics

large enough to conclude that the populations represented by the samples are different?”

Basic Logic

The H0 is that the populations are the same. There is no difference between the parameters

of the two populations If the difference between the sample

statistics is large enough, or, if a difference of this size is unlikely, assuming that the H0 is true, we will reject the H0 and conclude there is a difference between the populations.

Basic Logic

The H0 is a statement of “no difference” The 0.05 level will continue to be our

indicator of a significant difference We change the sample statistics to a Z

score, place the Z score on the sampling distribution and use Appendix A to determine the probability of getting a difference that large if the H0 is true.

The Five Step Model

1. Make assumptions and meet test requirements.

2. State the H0.

3. Select the Sampling Distribution and Determine the Critical Region.

4. Calculate the test statistic.5. Make a Decision and Interpret

Results.

Example: Hypothesis Testing in the Two Sample Case

Problem 9.7b. Middle class families average 8.7 email

messages and working class families average 5.7 messages.

The middle class families seem to use email more but is the difference significant?

Step 1 Make Assumptions and Meet Test Requirements Model:

Independent Random Samples The samples must be independent of each

other. LOM is Interval Ratio

Number of email messages has a true 0 and equal intervals so the mean is an appropriate statistic.

Sampling Distribution is normal in shape N = 144 cases so the Central Limit Theorem

applies and we can assume a normal shape.

Step 2 State the Null Hypothesis

H0: μ1 = μ2

The Null asserts there is no significant difference between the populations.

Step 2 State the Null Hypothesis

H1: μ1 μ2 The research hypothesis contradicts the

H0 and asserts there is a significant

difference between the populations.

Step 3 Select the S. D. and Establish the C. R.

Sampling Distribution = Z distribution Alpha (α) = 0.05 Z (critical) = ± 1.96

Step 4 Compute the Test Statistic

Use Formula 9.4 to compute the pooled estimate of the standard error.

Use Formula 9.2 to compute the obtained Z score.

Step 5 Make a Decision The obtained test statistic (Z = 20.00) falls

in the Critical Region so reject the null hypothesis.

The difference between the sample means is so large that we can conclude (at α = 0.05) that a difference exists between the populations represented by the samples.

The difference between the email usage of middle class and working class families is significant.

Factors in Making a Decision

The size of the difference (e.g., means of 8.7 and 5.7 for problem 9.7b)

The value of alpha (the higher the alpha, the more likely we are to reject the H0

Factors in Making a Decision

The use of one- vs. two-tailed tests (we are more likely to reject with a one-tailed test)

The size of the sample (N). The larger the sample the more likely we are to reject the H0.

Significance Vs. Importance

As long as we work with random samples, we must conduct a test of significance.

Significance is not the same thing as importance. Differences that are otherwise trivial or

uninteresting may be significant.

Significance Vs. Importance

When working with large samples, even small differences may be significant. The value of the test statistic (step 4) is

an inverse function of N. The larger the N, the greater the value of

the test statistic, the more likely it will fall in the C.R. and be declared significant.

Significance Vs Importance Significance and importance are different

things. In general, when working with random

samples, significance is a necessary but not sufficient condition for importance.

Significance Vs Importance A sample outcome could be:

significant and important significant but unimportant not significant but important not significant and unimportant