Question at position 12 Professor Galang wants to compare the average grades of three different teaching methods (traditional, flipped classroom, and online) used in a class of 90 students. Which statistical test would best determine whether the teaching methods produce different results?One-way ANOVAPearson correlationChi-square testPaired t-test单项选择题
A
One-way ANOVA
B
Pearson correlation
C
Chi-square test
D
Paired t-test

Question

Question at position 12 Professor Galang wants to compare the average grades of three different teaching methods (traditional, flipped classroom, and online) used in a class of 90 students. Which statistical test would best determine whether the teaching methods produce different results?One-way ANOVAPearson correlationChi-square testPaired t-test单项选择题

A

One-way ANOVA

B

Pearson correlation

C

Chi-square test

D

Paired t-test

BlackTom AI · Accepted Answer

To determine the appropriate statistical test, we first restate the question and options to ensure clarity about what is being compared.
Question: Professor Galang wants to compare the average grades of three different teaching methods (traditional, flipped classroom, and online) in a class of 90 students. Which statistical test would best determine whether the teaching methods produce different results?
Answer choices: 1) One-way ANOVA 2) Pearson correlation 3) Chi-square test 4) Paired t-test

Option 1: One-way ANOVA. This test is designed to compare the means across three or more independent groups to see if at least one group mean differs from the others. Since there are three teaching methods (independent groups) and a continuous outcome (grades), this is the correct framework for testing differences in means across more than two groups.

Option 2: Pearson correlation. Correlation assesses the strength and direction of a linear relationship between two continuous variables. It does not compare means across groups, so it’s not appropriate for testing whether teaching methods yield different average grades.

Option 3: Chi-square test. The chi-square test is used for categorical data to examine associations or independence between categorical variables (e.g., counts in categories).Grades are typically treated as a continuous or ordinal variable, and the design here involves comparing means across groups rather than frequencies in categories, so chi-square isn’t suitable.

Option 4: Paired t-test. The paired t-test compares means from the same subjects under two related conditions (e.g., pre/post measurements) or matched pairs. Our scenario involves three independent teaching methods applied to different students, so the paired t-test is not appropriate for comparing three independent groups.

In summary, the reasoning points to One-way ANOVA as the test that matches the study design: multiple independent groups (three teaching methods) with a continuous outcome (grades), aiming to detect any differences in means among the groups. The other tests are mismatched to this setup due to their purposes (relationship between variables, categorical data, or paired measurements).