An Update on Data Distribution and Techniques of Data Transformation

Abstract

The distribution in biostatistics can be defined as distribution of frequencies of values of a given variable in a sample. Distribution can be broadly classified into normal and skewed distribution. Normal distribution is a symmetrical bell shaped curve. ±1 standard deviation covers 65% of values around median value and ±2 S.D. covers 95% of values around median value. Mean, median & mode are equal for normal distribution curve. Parametric test like t test and ANOVA are based on the assumption that the data follows normal distribution. In skewed or asymmetrical distribution, there is clustering of cases in either right side or left side of the curve. In right sided skewness, the tail of curve is on the right side. In left skewed distribution, the tail is on the left side. Non-parametric test can be used in case of skewed data. Parametric test are more robust as compare to non-parametric test. The alternative is to transform the numerical variable into another scale where the values do satisfy the assumptions needed for the desired parametric or “normal” statistical methods. These technique include logarithm transformation, generalized linear modelling, and bootstrapping.