Have a go at choosing what might be the correct test to analyse the data from this experiment. Explain your choice.
This is invariably the step that students find the hardest we therefore return to this in interactive exercises in chapters 4 - 8.
Using the information in appendix b of the text:
B.1. What type of investigation am I designing?
In this investigation we are starting out with a question so this is an experiment.
B.2. Which type of hypotheses am I testing?
There are three types of hypotheses that you need to choose between. If you are not sure which type of hypotheses you will be testing read the information in B.2.1 - B.2.3 before deciding. For more information about hypotheses and hypothesis testing read Chapter 4.
- Does the data match an expected ratio?
or
- Is there an association between two or more variables?
or
- Do samples come from the same or different populations?
It is not always easy to decide which type of hypothesis you are testing. In this case the student wanted to compare the acceptability index of a particular plant in two species of snail and to compare the acceptability of a number of different plants species. He had no expectation and did not want to examine the association between the snails and plants. He was therefore testing the third type of hypotheses.
B.2.3. Do samples come from the same or different populations?
To test this type of hypothesis you need to decide if the data are likely to be parametric. To tell if your data are likely to be parametric (Normally distributed) you should refer to Box 3.2 in the book and in the Statistical Software section of the Online Resource Centre. When designing an investigation you can only use criterion a to decide whether your data may be parametric and in this case a decision can be made based on this criterion.
Criterion a. Are the data measured on an interval scale which is therefore quantitative and continuous, such as mm and grams?
In this example the answer is NO, the scale of measurement is an acceptability index. This is a based on the ratio of two proportions and is therefore not an interval scale.
B.2.3.2. Non-parametric tests
Choosing a non-parametric test depends on how many treatment variables you are planning to examine, how many categories in each variable, and how many replicates in each category. In this example there are two treatment variables, snails (with two categories) and plants (with 14 categories). The current design has 5 replicates. From the table you can see that the statistical test that is most likely to be appropriate is either the non-parametric two-way ANOVA or the Scheirer - Ray - Hare test.
Experimental design |
Test |
You have one treatment variable. You are going to compare two samples. The data is unmatched. You have 20 observations or less in each sample. |
Mann Whitney U test (8.1.) |
You have one treatment variable. You are going to compare two samples. The data is unmatched. The data is measured on a continuous scale and you have more than 30 observations in each sample. |
z test for unmatched data (7.1.) |
You have one treatment variable. You are going to compare two samples. The data is unmatched. You have more than 20 observations in each sample. |
Sokal & Rohlf, 1981. |
You have one treatment variable. You are going to compare two samples. The data is matched. You have less than 30 pairs of observations. |
Wilcoxen's rank paired test (8.2.) |
You have one treatment variable. You are going to compare two samples. The data is matched. You have more than 30 pairs of observations. |
z test for matched data (Chapter 7 (7.2)). |
You have one treatment variable. You are going to compare two or more samples. You wish to test general and specific hypotheses. |
One-way ANOVA (Kruskal Wallis test)( 8.3. and 8.4) |
You have more than one treatment variable. You are going to compare two or more samples. You wish to test general and specific hypotheses. You will be using a calculator. |
Two-way non parametric ANOVA (8.5. and 8.6) |
You have more than one treatment variable. You are going to compare two or more samples. You wish to test general hypotheses. You want to use a computer. |
Scheirer - Ray - Hare test (8.7.). |
The criteria for using the non parametric ANOVA (8.5.1.) are that you:
1. Wish to test for differences in population medians.
2. Have two treatment variables each with at least two categories.
3. Have an orthogonal design
4. Have non-parametric data that can be ranked.
5. Can test both general and specific predictions if there are equal numbers of observations in each sample. If there are not equal numbers of observations in each sample only specific predictions can be tested.
All these criteria are met. The design is orthogonal in that every plant species will be tested by both species of snail. The scale though non-parametric is quantitative and can be ranked. The current plan is for 5 replicates of each test and therefore both general and specific hypotheses can be tested.
The criteria for the Scheirer - Ray - Hare test (8.7.1.) are that you:
- Wish to test for differences in population medians.
- Have two treatment variables each with at least two categories.
- The design is orthogonal.
- Have non-parametric data that can be ranked.
It is clear that these criteria are therefore also met.