Deck 11: Hypothesis Testing With Chi-Square

Question 1

When using the χ² test statistic, "expected" frequencies are those that researchers observe in the sample.

Answer 1

False

Question 2

The χ² test statistic is useful for determining the direction of relationships.

Answer 2

False

Question 3

The concept of statistical significance relates to the matter of confidence.

Answer 3

True

Question 4

The null hypothesis is stated in Greek letters because the null hypothesis refers to relations in the population.

Question 5

The last step in hypothesis testing is looking up the critical value of test statistics.

Question 6

When two variables are unrelated to each other, the χ² test statistic is large.

Question 7

A 5% level of significance suggests a higher level of confidence than a 1% level of significance.

Question 8

The critical value is compared against the test statistic for the purpose of hypothesis testing.

Question 9

A relationship that is reported as being significant at "p = 0.945" is statistically significant.

Question 10

Degree of freedom is a concept associated with determining the critical value.

Question 11

Generally, a critical value will be larger at the 1% level of significance than at the 5% level of significance.

Question 12

It is easier to reject the null hypothesis with a large sample than with a small sample.

Question 13

We first determine practical significance and then determine statistical significance.

Question 14

Practical significance examines the strength of relationships.

Question 15

Practical significance examines by how much one variable changes as a result of changing another.

Question 16

A goodness-of-fit test tests can use χ² to determine whether a program or policy exceeds a standard or norm.

Question 17

Kendall's τ-c is a nonparametric statistic which should never be used because it is less useful than χ².

Question 18

Kendall's τ-c can vary from +1.00 to -1.00. Scores of less than |0.25| indicate weak relationships; scores between |0.25| and |0.50| indicate moderate relationships; and scores of greater than |0.50| indicate strong relationships.

Question 19

(Optional, Appendix): other nonparametric examples include Kendals τ-b, Goodman-Kruskal's τ and the McNemar test.

Question 20

(Optional, Appendix): When adding a control variable causes a previously significant relationship to become insignificant, the result is called explanation.