Normalization of data for ANOVA Is it necessary to do normalization before anova if data are not normal (came to know after normality test).
I must explain my experiment: I have different temperature ranges (10-50 $^\circ$C) given to insects; then I measure the number of days to complete a life cycle against each temperature. 
When I did ANOVA then I got $F$ that was almost 5000. One reviewer of our paper suggested that you should transform data before analysis. 
I did $\log_{10}(X+1)$ transformation  but data are still not normal. So what should I do?
 A: First, the data do not have to be normal for ANOVA (or, equivalently, linear regression). The assumptions are about the errors, which are represented by residuals.
Second, you should transform your data if it makes sense, substantively, to do so. You tell us, but it doesn't appear to me like there is a substantive reason to take logs.
Third, if the assumptions of ANOVA are violated, don't force the data to fit, use a method that makes different assumptions such as quantile regression, robust regression, regression trees ....
Fourth, if the number of days surviving is an integer, and the range isn't great, then you probably want to use a model for count data, such as Poisson or negative binomial regression.
Finally, if some of the data is censored (as is often the case with time to event data) then you need some form of survival analysis. 
A: I can not comment yet. Take this response down if needed. 
Usually, it is advised against using a statistical test to quantify normality of a distribution.
If you have a small sample size these tests do not have enough power to detect violation of normality and if the sample size becomes too large the test will become significant even if the distribution deviates only by little from the normal distribution. I would recommend just visually inspecting the data through a histogram or Q-Q plot.
Regarding the transformations. It really depends on the way your data is distributed. So  maybe uploading a picture of the distribution, in form of a histogram, could help.
A: Try other transformations. Look here for more ideas. For example: $\sqrt X, 1/X, 1/X^2\ldots$ if you really want ANOVA.
The other option is to non-parametric alternative to ANOVA: Kruskal-Wallis test. It works even for non-normal data.
A: Or eithe a) use a generalized least squares approach where you model the variance (eg it differs by group) or b) use a generalized linear model with the appropriate error distribution for your data. 
