Logarithmic transformations adjust skewed distributions
Analyze skewed data using more powerful parametric statistics
Logarithmic transformations are powerful statistical tools when employed and interpreted in the correct fashion. Transforming the distribution of a continuous variable due to violating normality allows researchers to account for outlying observations and use more powerful parametric statistics to assess any significant associations.
Also, some continuous variables are naturally skewed. One particular outcome that is prevalent in medicine is LOS or length of stay in the hospital. Most patients will be in the hospital between one and three days, VERY FEW will be in the hospital for weeks and months at a time. In order to include these outlying patients in analyses, transformations must be performed. Naturally skewed variables can be analyzed with parametric statistics with transformations!
An important thing to remember when conducting logarithmic transformations is that only the p-value associated with inferential statistics can be interpreted, NOT the means and standard deviations of the transformed observations. Instead, researchers should report the median and interquartile range for the distribution.
Also, some continuous variables are naturally skewed. One particular outcome that is prevalent in medicine is LOS or length of stay in the hospital. Most patients will be in the hospital between one and three days, VERY FEW will be in the hospital for weeks and months at a time. In order to include these outlying patients in analyses, transformations must be performed. Naturally skewed variables can be analyzed with parametric statistics with transformations!
An important thing to remember when conducting logarithmic transformations is that only the p-value associated with inferential statistics can be interpreted, NOT the means and standard deviations of the transformed observations. Instead, researchers should report the median and interquartile range for the distribution.
