## When the null hypothesis for an Anova analysis comparing four treatment means is rejected?

When the null hypothesis for an ANOVA analysis comparing four treatment means, is rejected: 4 comparisons of treatment means can be made. 8 comparisons of treatment means can be made. 12 comparisons of treatment means can be made.

## What is the value of F when the null hypothesis is true?

The F-statistic is the test statistic for F-tests. In general, an F-statistic is a ratio of two quantities that are expected to be roughly equal under the null hypothesis, which produces an F-statistic of approximately 1.

## What is stated by the null hypothesis for an Anova?

The null hypothesis in ANOVA is always that there is no difference in means. The research or alternative hypothesis is always that the means are not all equal and is usually written in words rather than in mathematical symbols.

## What are the two types of effects you must be able to identify from an Anova?

The results from a Two Way ANOVA will calculate a main effect and an interaction effect. With the interaction effect, all factors are considered at the same time. Interaction effects between factors are easier to test if there is more than one observation in each cell.

## What is the purpose of Anova?

Analysis of variance, or ANOVA, is a statistical method that separates observed variance data into different components to use for additional tests. A one-way ANOVA is used for three or more groups of data, to gain information about the relationship between the dependent and independent variables.

## Why do we use Tukey test?

Tukey’s test compares the means of all treatments to the mean of every other treatment and is considered the best available method in cases when confidence intervals are desired or if sample sizes are unequal (Wikipedia).

## Why use a Tukey post hoc?

The purpose of Tukey’s test is to figure out which groups in your sample differ. It uses the “Honest Significant Difference,” a number that represents the distance between groups, to compare every mean with every other mean. Like Tukey’s this post-hoc test is used to compare means.

## What does post hoc mean?

1 : relating to or being the fallacy of arguing from temporal sequence to a causal relation. 2 : formulated after the fact a post hoc rationalization.

## What is the Bonferroni test used for?

The Bonferroni test is a statistical test used to reduce the instance of a false positive. In particular, Bonferroni designed an adjustment to prevent data from incorrectly appearing to be statistically significant.

## When should you run post hoc tests?

Because post hoc tests are run to confirm where the differences occurred between groups, they should only be run when you have a shown an overall statistically significant difference in group means (i.e., a statistically significant one-way ANOVA result).

## What does post hoc mean in statistics?

after the event

## How do you analyze one-way Anova results?

Interpret the key results for One-Way ANOVA

- Step 1: Determine whether the differences between group means are statistically significant.
- Step 2: Examine the group means.
- Step 3: Compare the group means.
- Step 4: Determine how well the model fits your data.
- Step 5: Determine whether your model meets the assumptions of the analysis.

## How do you interpret a one-way Anova in SPSS?

Quick Steps

- Click on Analyze -> Compare Means -> One-Way ANOVA.
- Drag and drop your independent variable into the Factor box and dependent variable into the Dependent List box.
- Click on Post Hoc, select Tukey, and press Continue.
- Click on Options, select Homogeneity of variance test, and press Continue.

Table of Contents