Parametric Statistics requires the assumption that quantitative data exhibits normal distribution
- parametric statistic is a stronger way of testing hypothesis than non-parametric statistic
quantitative data can be either:
- normal
- right skewed
- left skewed
if data does not exhibit normal distribution, we can either:
- use a mathematical transformation on the data that leads to normal distribution
- use another method that does not require normal distribution
- rank-based method
- non-parametric testing
3 commonly used transformations for quantitative data:
- logarithm
- square root
- reciprocal
we call these transformations variance-stabilizing because, their purpose is to make variances the same
transform values of a variable to take on normal distribution
https://machinelearningmastery.com/how-to-transform-data-to-fit-the-normal-distribution/
plot data as histogram and see what transformation is needed
