Hello everyone!
As seen below, we have 3 measurement results for 3 different experimental groups;
Groups Results
A 1
A 2
A 4
B 3
B 2
B 5
C 9
C 9
C 9
What would be the most appropriate statistical test analysis for these groups with low number (n=3) of observations?
To analyze the data from these three experimental groups with a low sample size (n=3), the most appropriate statistical test would depend on the distribution of the data and the hypothesis being tested. If the data is approximately normally distributed and the variances are similar across groups, a one-way ANOVA can be used to compare the means of the groups. However, with small sample sizes, normality and homogeneity of variance assumptions might be difficult to meet. In that case, a non-parametric test like the Kruskal-Wallis test is more appropriate, as it does not assume normality or equal variances.
Before proceeding, it would be good to visualize the data using boxplots and conduct tests for normality (e.g., Shapiro-Wilk test) and homogeneity of variances (e.g., Levene’s test). This will help decide between parametric and non-parametric approaches. Keep in mind that non-parametric tests might have less power, but they are more robust for small sample sizes or non-normal data.
Dear Jochen Wilhelm,
Thank you for taking the time to correct me. I greatly appreciate your guidance and insights. I had attempted to address the question based on my limited understanding, and your clarification has helped enhance my perspective.
Thank you
Debajit
Hello Jochen Wilhelm
Perhaps this is why it is better to test for the scales (e.g. variances) before testing the hypothesis of equality of locations (e.g. means). Am I right?
You can use t test to compre two groups or can use ANOVA to compare all groups simultaneously
www.docnavigator.co.in
- Badges
- Science topic