An Update on Data Distribution and Techniques of Data Transformation
DOI:
https://doi.org/10.21276/zqxbnt82Keywords:
data distribution, data transformationAbstract
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
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