Contents
- 1 What statistical analysis should I use to compare pre and post tests?
- 2 Which pair of statistical measures is most appropriate to use to compare the scores of two groups?
- 3 What does it mean when sample one is not related to sample two?
- 4 Is the confidence interval for the difference in means the same?
What statistical analysis should I use to compare pre and post tests?
The marks for a group of students before (pre) and after (post) a teaching intervention are recorded below: Marks are continuous (scale) data. Continuous data are often summarised by giving their average and standard deviation (SD), and the paired t-test is used to compare the means of the two samples of related data.
Which pair of statistical measures is most appropriate to use to compare the scores of two groups?
You can use Student-t test to compare the means of the scores of different groups or the same group with different criteria as you mentioned . The idea is to find if there is a statistically significant difference ( p-value < 0.05) which will reject the null hypothesis.
When do you need to know if two samples are independent?
Anytime you compare two populations you need to know if the samples are independent or dependent. The formulas you use are different for different types of samples. If how you choose one sample has no effect on the way you choose the other sample, the two samples are independent.
This will mean that sample one has no affect on sample two. The sample values from one sample are not related or paired with values from the other sample. If you choose the samples so that a measurement in one sample is paired with a measurement from the other sample, the samples are dependent or matched or paired.
Is the confidence interval for the difference in means the same?
The confidence interval for the difference in means has the same random variables and means and the same assumptions as the hypothesis test for two paired samples. If you have already completed the hypothesis test, then you do not need to state them again.
Why are the values of two dependent samples paired?
One way to think about it is that in dependent samples, the individuals from one sample are the same individuals from the other sample, though there can be other reasons to pair values. This makes the sample values from each sample paired.