Tests of Significance MCQs

A test with high specificity has a low probability of falsely classifying individuals without the disease as positive (Type I error). Explanation for why other options are wrong: b. Type II error: Type II error refers to the probability of incorrectly failing to reject a false null hypothesis, which is more related to sensitivity than specificity. High specificity reduces the likelihood of Type I errors, not Type II errors. c. 1-β represents the power of a statistical test, which is related to sensitivity rather than specificity. Higher specificity does not directly influence the power of the test. d. 1-α Represents the significance level of a statistical test, which is determined by the researcher and is not directly influenced by the specificity of the test. Reference: Park's Preventive and social medicine, 27th EDITION Pg 156

In a contingency table, the number of degrees of freedom is calculated using the formula: Degrees of freedom = (r - 1) * (c - 1) where r is the number of rows and c is the number of columns in the contingency table. Given that the contingency table in this scenario is 3x4 (3 rows and 4 columns), the calculation for degrees of freedom would be: Degrees of freedom = (3 - 1) * (4 - 1) = 2*3 = 6 Therefore, the correct answer is c. 6 Reference: Park's Preventive and social medicine, 27th EDITION Pg 980

In a chi-square test for a contingency table, the degrees of freedom (df) are calculated using the formula: df = (number of rows - 1) × (number of columns - 1) In this case, the contingency table has 4 rows and 4 columns. So, applying the formula: df = (4 - 1) × (4 - 1) = 3 × 3 = 9 Therefore, the degrees of freedom for a contingency table of 4 rows and 4 columns would be 9. Reference: Park's Preventive and social medicine, 27th EDITION Pg 980

When the degrees of freedom (df) is 1 in a chi-square test, the critical value of χ² for a probability of 0.05 (corresponding to a two-tailed test) is approximately 3.84. This value is commonly used as a threshold to determine statistical significance at the 0.05 level for hypothesis testing in chi-square tests. Therefore, the correct critical value of χ² for a chi-square test with df = 1 and a probability of 0.05 is 3.84, making option (c) the correct answer. Reference: Park's Preventive and social medicine, 27th EDITION Pg 980

When the degrees of freedom (df) is 1 in a t-test, the critical value of t for a probability of 0.05 (corresponding to a two-tailed test) is approximately 1.96. This value is commonly used as a threshold to determine statistical significance at the 0.05 level for hypothesis testing. Therefore, the correct critical value of t for a t-test with df = 1 and a probability of 0.05 is 1.96, making option (a) the correct answer. Reference: Park's Preventive and social medicine, 27th EDITION Pg 980

Explanation: The Chi-Square test is the most appropriate. This test is used to determine if there is a significant association between two categorical variables. Here, the variables are "Geographical Area" (Rural/Urban) and "Nutritional Status" (Malnourished/Not Malnourished). The data is presented as frequencies or proportions in a 2x2 contingency table. Explanation of other options: Paired T test (Option B): A paired t-test is used to compare the means of the same group at two different times (e.g., before and after an intervention), not two different groups. Standard error of the mean (Option C): This is not a test of significance; it is a measure of the variability of a sample mean estimate. ANOVA (Option D): ANOVA is used to compare the means of more than two independent groups (e.g., rural, urban, and semi-urban areas).