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Null hypothesis for split-plot ANOVA
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Null-hypothesis for a Split-Plot One-Way Analysis of Variance (ANOVA)
Conceptual Explanation
With hypothesis testing we are setting up a null-hypothesis –
With hypothesis testing we are setting up a null-hypothesis – the probability that there is no effect or relationship –
With hypothesis testing we are setting up a null-hypothesis – the probability that there is no effect or relationship – and then we collect evidence that leads us to either accept or reject that null hypothesis.
As you may recall, a One-Way Split-Plot ANOVA is like a Factorial ANOVA except that instead of having two independent variables (e.g., age and gender) you have at least one independent variable with two or more levels that are independent of one another (e.g., gender – male and female) and another independent variable with two or more levels that are repeated (e.g., pre-post test).
As you may recall, a One-Way Split-Plot ANOVA is like a Factorial ANOVA except that instead of having two independent variables (e.g., age and gender) you have at least one independent variable with two or more levels that are independent of one another (e.g., gender – male and female) and another independent variable with two or more levels that are repeated (e.g., pre-post test).
Here is a template for writing a null-hypothesis for a Split-Plot ANOVA.
Here is a template for writing a null-hypothesis for a Split-Plot ANOVA.
There is no statistically significant effect for [Insert independent main effect]
Here is a template for writing a null-hypothesis for a Split-Plot ANOVA.
There is no statistically significant effect for [Insert independent main effect]There is no statistically significant effect for [Insert repeated main effect]
Here is a template for writing a null-hypothesis for a Split-Plot ANOVA.
There is no statistically significant effect for [Insert independent main effect]There is no statistically significant effect for [Insert repeated main effect]There is no statistically significant interaction effect between [Insert independent main effect]
Here is a template for writing a null-hypothesis for a Split-Plot ANOVA.
There is no statistically significant effect for [Insert independent main effect]There is no statistically significant effect for [Insert repeated main effect]There is no statistically significant interaction effect between [Insert independent main effect] and [Insert repeated main effect]
Example #1
Problem:
Problem: An Agricultural Scientist has been asked to evaluate the effect of an innovative fertilizer on the yields of three varieties of oats. During the first growing season they apply the traditional fertilizer. The second season they apply the new fertilizer. The yields of the three are compared between the two growing seasons.
Template:
Template: There is no statistically significant effect for [Insert independent main effect]
Template: There is no statistically significant effect for [Insert independent main effect]There is no statistically significant effect for [Insert repeated main effect]
Template: There is no statistically significant effect for [Insert independent main effect]There is no statistically significant effect for [Insert repeated main effect]There is no statistically significant interaction effect between [Insert independent main effect]
Template: There is no statistically significant effect for [Insert independent main effect]There is no statistically significant effect for [Insert repeated main effect]There is no statistically significant interaction effect between [Insert independent main effect] and [Insert repeated main effect]
Null-hypothesis:
Null-hypothesis: There is no statistically significant effect for oat type.
Null-hypothesis: There is no statistically significant effect for oat type.There is no statistically significant effect for growing year.
Null-hypothesis: There is no statistically significant effect for oat type.There is no statistically significant effect for growing year.There is no statistically significant interaction effect between oat type and growing year.
Example #2
Problem:
Problem: A pastor of a congregation wants to know the effect of an new type of sermon on parishioner religious behavior. His plan is to give his morning congregation the new type of sermon and his evening congregation (the control group) a traditional sermon. Prior to commencing the two sermon types, he sends a self-report survey to parishioners to assess their religious behavior. Three months later he sends out the same survey and compares the results.
Template:
Template: There is no statistically significant effect for [Insert independent main effect]
Template: There is no statistically significant effect for [Insert independent main effect]There is no statistically significant effect for [Insert repeated main effect]
Template: There is no statistically significant effect for [Insert independent main effect]There is no statistically significant effect for [Insert repeated main effect]There is no statistically significant interaction effect between [Insert independent main effect]
Template: There is no statistically significant effect for [Insert independent main effect]There is no statistically significant effect for [Insert repeated main effect]There is no statistically significant interaction effect between [Insert independent main effect] and [Insert repeated main effect]
Null-hypothesis:
Null-hypothesis: There is no statistically significant effect for sermon type.
Null-hypothesis: There is no statistically significant effect for sermon type.There is no statistically significant effect for time of survey administration.
Null-hypothesis: There is no statistically significant effect for sermon type.There is no statistically significant effect for time of survey administration.There is no statistically significant interaction effect between sermon type
Null-hypothesis: There is no statistically significant effect for sermon type.There is no statistically significant effect for time of survey administration.There is no statistically significant interaction effect between sermon type and time of survey administration.
