Module 6.1: Hypothesis Tests and Types of Errors10 min
This section introduces the fundamental steps of hypothesis testing: stating the hypothesis, selecting the test statistic, specifying the significance level, stating the decision rule, collecting data, and making a decision. The null hypothesis (H0) is the proposition to be tested and usually includes an 'equal to' condition, while the alternative hypothesis (Ha) is what is concluded if the null is rejected. Tests can be one-tailed or two-tailed depending on whether the alternative hypothesis specifies a direction.

Errors are an intrinsic part of testing. A Type I error involves rejecting a true null hypothesis, with the probability denoted by alpha (the significance level). A Type II error involves failing to reject a false null hypothesis. The power of a test is the probability of correctly rejecting a false null hypothesis. The relationship between confidence intervals and hypothesis tests is also explored; a two-tailed test at the alpha significance level is complementary to a (1 - alpha) confidence interval.

Key Points

  • Null hypothesis (H0) represents the 'no effect' or status quo and includes the equality condition.
  • Alternative hypothesis (Ha) is what the researcher typically wants to prove.
  • Type I error: Rejecting H0 when it is true (Probability = alpha).
  • Type II error: Failing to reject H0 when it is false (Probability = beta).
  • Power of a test = 1 - Probability of Type II error.
  • A statistically significant result does not necessarily imply economic significance.
Module 6.2: P-Values and Tests of Means10 min
This section focuses on the p-value, which is the smallest level of significance at which the null hypothesis can be rejected. It serves as a measure of the strength of the evidence against the null hypothesis. The section also details the specific test statistics for population means. The z-statistic is used when the population is normally distributed with a known variance. The t-statistic (Student's t-distribution) is used when the population variance is unknown. The t-distribution has fatter tails than the normal distribution, leading to more conservative critical values, especially for small sample sizes. The Central Limit Theorem allows the use of these tests for non-normal populations if the sample size is large (typically n >= 30).

Key Points

  • The p-value is the probability of obtaining a test statistic as extreme as the one observed, assuming H0 is true.
  • Reject H0 if the p-value is less than the significance level (alpha).
  • Use z-statistic when population variance is known.
  • Use t-statistic when population variance is unknown (df = n - 1).
  • For large samples (n >= 30), the t-test and z-test produce similar results.
Module 6.3: Mean Differences and Paired Comparisons10 min
This section expands hypothesis testing to comparing two means. For independent samples drawn from normally distributed populations with equal variances, a t-test using pooled variance is appropriate. The degrees of freedom for this test are (n1 + n2 - 2). When samples are dependent (e.g., measuring the same subjects before and after an event, or matched pairs), a paired comparisons test is used. This test focuses on the mean of the differences between the paired observations. The test statistic for paired comparisons is a simple t-statistic testing whether the mean difference is zero, with degrees of freedom equal to the number of pairs minus one.

Key Points

  • Difference in means test (independent samples) uses a t-statistic based on pooled variance if variances are assumed equal.
  • Degrees of freedom for independent means test: n1 + n2 - 2.
  • Paired comparisons test is used for dependent samples (e.g., before/after).
  • The paired test analyzes the mean of the differences (d-bar) against a hypothesized difference (usually 0).
  • Degrees of freedom for paired test: n - 1 (where n is the number of pairs).
Module 6.4: Variance, Correlation, and Independence15 min
This section covers tests for parameters other than means. The chi-square test is used to test hypotheses about a single population variance. The chi-square distribution is asymmetrical and bounded by zero. To compare the variances of two normally distributed populations, the F-test is used. The F-statistic is the ratio of the sample variances (larger variance in the numerator), and the decision rule is based on the F-distribution.

Tests for correlation determine if the population correlation coefficient differs from zero, using a t-statistic with (n - 2) degrees of freedom. The section also introduces the Spearman rank correlation coefficient for nonparametric tests of correlation. Finally, the chi-square test for independence uses a contingency table to determine if two categorical characteristics are independent.

Key Points

  • Test for single variance uses the chi-square statistic (df = n - 1).
  • Test for equality of two variances uses the F-statistic (s1 squared / s2 squared).
  • Parametric test for correlation uses a t-statistic (df = n - 2).
  • Spearman rank correlation is a nonparametric test for ranked data.
  • Test for independence uses a chi-square contingency table analysis.

Questions

Question 1

Which of the following statements best describes the null hypothesis?

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Question 2

A researcher wants to test if the mean return on a portfolio is different from zero. The appropriate set of hypotheses is:

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Question 3

A Type I error is defined as:

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Question 4

The power of a hypothesis test is defined as the probability of:

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Question 5

If the significance level of a test is 5 percent and the probability of a Type II error is 20 percent, the power of the test is:

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Question 6

For a two-tailed test using the standard normal distribution at the 5 percent level of significance, the critical z-values are:

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Question 7

A test statistic is calculated as:

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Question 8

The p-value is best described as:

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Question 9

An analyst conducts a hypothesis test and calculates a p-value of 0.03. If the chosen significance level is 0.05, the analyst should:

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Question 10

Which of the following distributions is appropriate for testing a hypothesis about a population mean when the variance is unknown and the sample is large?

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Question 11

A sample of 25 observations has a mean of 10 and a standard deviation of 4. The researcher wants to test if the population mean is greater than 8 at the 5 percent significance level. The standard error of the sample mean is:

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Question 12

Using the data from the previous question (Mean=10, Hypothesized=8, SE=0.8), the calculated test statistic is:

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Question 13

In a one-tailed test with 24 degrees of freedom at the 5 percent significance level, the critical t-value is 1.711. Based on a calculated t-statistic of 2.50, the researcher should:

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Question 14

The distinction between statistical significance and economic significance implies that:

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Question 15

When testing the difference between two population means using two independent samples, the degrees of freedom for the t-test (assuming equal variances) are calculated as:

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Question 16

A paired comparisons test is most appropriate when:

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Question 17

For a paired comparisons test with 20 pairs of observations, the degrees of freedom are:

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Question 18

To test the hypothesis that the variance of a normally distributed population is equal to a specific value, the appropriate test statistic is based on the:

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Question 19

A sample of 15 observations has a standard deviation of 6. The researcher wants to test if the population variance is equal to 25. The calculated chi-square statistic is closest to:

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Question 20

The F-statistic used to test the equality of two variances is calculated as:

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Question 21

Sample A has a variance of 25 (n=21) and Sample B has a variance of 16 (n=16). The calculated F-statistic to test if the variances are equal is:

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Question 22

To test the hypothesis that the population correlation coefficient equals zero, the appropriate test statistic follows a:

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Question 23

A sample of 42 observations shows a correlation coefficient of 0.35. The researcher wants to test if the correlation is different from zero. The degrees of freedom for this test are:

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Question 24

Nonparametric tests are preferred when:

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Question 25

The Spearman rank correlation coefficient is best described as:

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Question 26

A contingency table is used to test:

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Question 27

In a chi-square test for independence with 3 rows and 3 columns, the degrees of freedom are:

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Question 28

Data snooping bias refers to:

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Question 29

Survivorship bias in mutual fund studies typically leads to:

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Question 30

Look-ahead bias occurs when:

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Question 31

If a test statistic is 1.80 and the critical value is 1.65 (one-tailed upper), the decision is to:

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Question 32

Which test statistic is appropriate to test if the mean of a population is equal to zero when the variance is unknown and n=20?

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Question 33

If the null hypothesis is H0: mean <= 0, the alternative hypothesis Ha is:

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Question 34

The critical value for a two-tailed z-test at the 1 percent significance level is closest to:

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Question 35

A confidence interval for a population parameter can be expressed as:

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Question 36

If a 95 percent confidence interval for a mean does not include zero, then:

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Question 37

When the sample size is small and the population is not normally distributed, which test statistic should be used?

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Question 38

For a test of the equality of two variances, the critical F-value depends on:

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Question 39

A researcher assumes that two samples are independent, normally distributed, and have equal variances. The pooled variance is calculated as:

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Question 40

With 20 tests at the 10 percent significance level, the expected number of false positives (Type I errors) if the null hypothesis is true for all is:

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Question 41

Using a higher significance level (e.g., 10 percent instead of 5 percent) will:

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Question 42

A chi-square test for a single variance is:

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Question 43

If a calculated t-statistic is -2.4 and the critical values are +/- 2.1, the decision is:

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Question 44

In a contingency table, the expected frequency for a cell is calculated as:

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Question 45

Which of the following is an example of a nonparametric test?

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Question 46

If sample data has a t-statistic of 2.363 for a correlation test with 40 degrees of freedom (critical value 2.021), we conclude:

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Question 47

Economic significance differs from statistical significance in that:

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Question 48

When testing if the mean of a single population is 50, the null hypothesis should be stated as:

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Question 49

If a researcher decreases the probability of a Type I error (alpha), the probability of a Type II error (beta) will typically:

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Question 50

The test statistic for the Spearman rank correlation follows which distribution when n > 30?

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