The document discusses null hypotheses for a split-plot one-way ANOVA. It provides a template for writing a null hypothesis, which states that there is no statistically significant effect for the independent main effect, repeated main effect, or interaction between the two main effects. Two examples are given to demonstrate how to apply the template to problems involving an agricultural scientist evaluating fertilizer types on oat yields, and a pastor evaluating a new sermon type on parishioner behavior.

1. Null-hypothesis for a Split-Plot One-
Way Analysis of Variance (ANOVA)
Conceptual Explanation

2. With hypothesis testing we are setting up a null-hypothesis
–

3. With hypothesis testing we are setting up a null-hypothesis
– the probability that there is no
effect or relationship –

4. 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.

5. 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).

6. 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).

7. Here is a template for writing a null-hypothesis
for a Split-Plot ANOVA.

8. 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]

9. 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]

10. 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]

11. 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]

12. Example #1

13. Problem:

14. 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.

15. Template:

16. Template:
There is no statistically significant effect for
[Insert independent main effect]

17. Template:
There is no statistically significant effect for
[Insert independent main effect]
There is no statistically significant effect for
[Insert repeated main effect]

18. 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]

19. 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]

20. Null-hypothesis:

21. Null-hypothesis:
There is no statistically significant effect for oat
type.

22. Null-hypothesis:
There is no statistically significant effect for oat
type.
There is no statistically significant effect for
growing year.

23. 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.

24. Example #2

25. Problem:

26. 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.

27. Template:

28. Template:
There is no statistically significant effect for
[Insert independent main effect]

29. Template:
There is no statistically significant effect for
[Insert independent main effect]
There is no statistically significant effect for
[Insert repeated main effect]

30. 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]

31. 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]

32. Null-hypothesis:

33. Null-hypothesis:
There is no statistically significant effect for
sermon type.

34. Null-hypothesis:
There is no statistically significant effect for
sermon type.
There is no statistically significant effect for time
of survey administration.

35. 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

36. 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.