I have data from two replica experiments conducted at different times. Each has two factors (A and B). Factor A has 20 observations which was done in replicates of three. Factor B has 3 observations. I want to compare at how each observation in factor A differ within their respective experiment times per observation of factor B and also compare how the same observation in factor A compare to each other in the two experiment. I have taken two approaches:
1: NAME
Check for Consistency
- Visually inspect the data from both experiments to see if there are any outliers or inconsistencies. You can do this by creating scatter plots or box plots of the data from both experiments.
- Calculate summary statistics for each experiment, such as the mean, median, and standard deviation. Compare the summary statistics to see if there are any major differences between the two experiments.
2. Perform Statistical Tests
- Conduct a statistical test to determine if there is a statistically significant difference between the results of the two experiments. The type of statistical test you will use will depend on the nature of your data. For example, if your data is normally distributed, you can use a t-test. If your data is not normally distributed, you can use a non-parametric test, such as a Mann-Whitney U test.
3. Consider Potential Explanations
- If you find a statistically significant difference between the results of the two experiments, try to identify potential explanations for the difference. Consider factors such as:Differences in the way the experiments were conducted Differences in the materials or equipment used Differences in the participants in the experiments
4. Draw Conclusions
- Based on your analysis of the data, draw conclusions about the replicability of your findings. If the results of the two experiments are consistent, you can be more confident in your conclusions. If the results are not consistent, you may need to conduct further research to determine why the results differ
عذراء مهدي العذاري thank you so much for the answer. The two experiment are expected to be different. In this case I am testing effect of 10 strains on heights of a plant. I did check for homogeneity for the individual experiments and they are quite homogenous. Did a normality test using histograms and they are normally distributed. My main issue is when it comes to the statistical calculations. Should I combine the two data and run one anova model on all of them making the strain and "experiment time" a factor or should run three anovas i.e experiment 1 anova, experiment 2 anova and lastly strains-by-strain anova (i.e 10 anovas for each strain comparing its performance in experiment 1 and 2).
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