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## Should I always transform my variables to make them normal?

No, **you don’t have to transform your observed variables** just because they don’t follow a normal distribution. Linear regression analysis, which includes t-test and ANOVA, does not assume normality for either predictors (IV) or an outcome (DV). … Yes, you should check normality of errors AFTER modeling.

## Do I need to log transform independent variables?

**No, log transformations are not necessary for independent variables**. In any regression model, there is no assumption about the distribution shape of the independent variables, just the dependent variable.

## Should non normal data transform?

When control charts are used with non-normal data, they can give false special-cause signals. Therefore, **the data must be transformed to follow the normal distribution**.

## What if my dependent variable is not normally distributed?

In short, when a dependent variable is not distributed normally, **linear regression** remains a statistically sound technique in studies of large sample sizes. Figure 2 provides appropriate sample sizes (i.e., >3000) where linear regression techniques still can be used even if normality assumption is violated.

## Is normality test necessary?

We usually apply normality tests to the results of processes that, under the null, generate random variables that are only asymptotically or nearly normal (with the ‘asymptotically’ part dependent on some quantity which we cannot make large); In the era of cheap memory, big data, and fast processors, normality tests …

## Can I transform data twice?

If the transformation is invertible i.e. a convolution, then **yes**. Thank you all for your guidance! Log-transforming count data is discouraged.

## Why do we transform data in regression?

We usually transform information for many purposes, such as recode, compute, if, and weight. With compute, as an example,you can create new variables. As others have noted, people often transform in hopes of **achieving normality** prior to using some form of the general linear model (e.g., t-test, ANOVA, regression, etc).