- ANOVA: "A researcher wants to compare the mean effects for two different medications for anxiety by randomly assigning participants to receive one or the other."
- One sample t-test: "A city manager wonders whether there is a relationship between whether residents would be in favor of or oppose a new bill, and whether they live in a rural, suburban, or urban area."
- Related samples t-test: "A market analyst wants to determine whether a sample has a preference for one of five types of designs for a new company logo by having each person vote for their favorite."
- Independent samples t-test: "A market analyst wants to determine whether a sample has a preference for one of five types of designs for a new company logo by having them rate how much they like/dislike each logo, and comparing the mean rating for each logo."
What is a mean?
A mean is also referred to as an arithmetic average and it can be defined as a ratio of the sum of the total number in a data set (population) to the frequency of the data set.
What is an ANOVA?
ANOVA is an abbreviation for analysis of variance which was developed by the notable statistician Ronald Fisher. The analysis of variance (ANOVA) is a collection of statistical models with their respective estimation procedures that are used for the analysis of the difference between the group of means found in a sample.
In Statistics, the analysis of variance (ANOVA) procedure is typically used as a statistical tool to determine whether or not the means of two or more populations are equal, especially by splitting the observed variability of a data set into random and systematic factors.
What is the independent samples t-test?
The independent samples t-test can be defined as a statistical technique that is used for comparing the means of two (2) independent samples, so as ascertain whether there's sufficient statistical evidence that their population means are significantly different.
In this context, we can infer and logically deduce that the statistical test which is most appropriate for each scenarios are matched as follows:
1. ↔ D.
2. ↔ A.
3. ↔ C.
4. ↔ B.
Read more on ANOVA here: brainly.com/question/4543160
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Complete Question:
Matching Instructions: Identity which statistical test is most appropriate for the following scenarios. Each option will only be used once
