When to use t test on the logs?
The t-test on the logs compares geometric means, not the (usual) arithmetic means.” Transformations to normality should always be followed by an investigation of the normality assumption, to assess whether the transformed data looks “normal enough”. This can be done using for instance histograms, QQ-plots and tests for normality.
When do you not use the log transform?
If you are interested in whether the means of your two variables are equal, you should not transform one of them using the log-transform.
How to do a paired t test in GraphPad Prism 9?
1.Transform all the values to their logarithm: Y=log (Y). 2.Perform a paired t test on the logarithms. 3.The antilogarithm of the difference between logarithms is the geometric mean of the ratios. 4.Calculate the antilogarithm of each confidence limit of the difference between the means of the logarithms.
When do you use the paired t test?
The paired t test makes sense when the difference is consistent. The control values might bounce around, but the difference between treated and control is a consistent measure of what happened. With some kinds of data, the difference between control and treated is not a consistent measure of effect.
Is it OK to use t-test on histograms?
I gather you have a lot of data and the histograms look OK even if the test of the skewness is significant. You are probably OK to use the t -test straight away. If you do choose to use a data transformation before running your test, you should apply the same transformation to both groups.
Why do you want your data to be normal for a t-test?
The reason you want your data to be normal for a t -test is to ensure the sampling distributions of the means will be normal. However, due to the central limit theorem, the sampling distribution of the means will often become normal with enough data even if the original data are not normal.