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Does log transformation make data normal?
The log transformation is, arguably, the most popular among the different types of transformations used to transform skewed data to approximately conform to normality. If the original data follows a log-normal distribution or approximately so, then the log-transformed data follows a normal or near normal distribution.
What does a log transformation do?
Log transformation is a data transformation method in which it replaces each variable x with a log(x). In other words, the log transformation reduces or removes the skewness of our original data. The important caveat here is that the original data has to follow or approximately follow a log-normal distribution.
How do you handle negative data?
A common technique for handling negative values is to add a constant value to the data prior to applying the log transform. The transformation is therefore log(Y+a) where a is the constant. Some people like to choose a so that min(Y+a) is a very small positive number (like 0.001). Others choose a so that min(Y+a) = 1.
Can you use Brack transformed mean in ANOVA?
Or do the log transformation for the raw data, compute the mean and back transform that mean and do the ANOVA on that And for the graphs, can I use the brack transformed means depending one which one is the best option
Which is data transformation method do you use when variances?
I have gene expression data which includes negative values. When I run Factorial ANOVA, for some genes, Tests of Homogeneity of Variances (Cochran C, Hartley, Bartlett) suggested a p-value lower than 0.05. However, among them, some are significant according to Post-hoc test (Newman-Keuls test).
Do you do ANOVA on non normal data?
If yes before to do an ANOVA which is pretty robust against non normal data, you should review why this is not normally distributed. Did you perform a Measurement System Analysis before? Are you sure your resolution is enough? Measurement problems are often responsible for having non normal data where normality is expected.
What to do when data fail tests for homogeneity of variance?
What to do when data fail tests for homogeneity of variance (part of one-way ANOVA)? One-way ANOVA assumes that the data come from populations that are Gaussian and have equal variances. GraphPad Prism tests this assumption with Bartlett’s test.