skewness and kurtosis
Descriptive Statistics
Check for errors and outliers • Describe & summarise • Spread of the data • Ensure appropriate analysis • Data parametric or non‐parametric
negatively skewed <0
Negative Skewness is when the tail of the left side of the distribution is longer or fatter than the tail on the right side. The mean and median will be less than the mode. right skewness <0
Measure of Dispersion
Variation, Range, Standard Deviation
normal distribution
skewness 0
ordinal scales
the order of the values is significant but the differences between each one is not really known. typically measures of non-numeric concepts like satisfaction
nominal variables
used for labelling variables, without any quantitative value.
positively skewed
Positive Skewness means when the tail on the right side of the distribution is longer or fatter. The mean and median will be greater than the mode. left skewness >0
Skewness
Skewness is the degree of distortion from the symmetrical bell curve or the normal distribution. It measures the lack of symmetry in data distribution. It differentiates extreme values in one versus the other tail. A symmetrical distribution will have a skewness of 0. There are two types of Skewness: Positive and Negative
How to interpret skewness values
So, when is the skewness too much? The rule of thumb seems to be: If the skewness is between -0.5 and 0.5, the data are fairly symmetrical. If the skewness is between -1 and -0.5(negatively skewed) or between 0.5 and 1(positively skewed), the data are moderately skewed. If the skewness is less than -1(negatively skewed) or greater than 1(positively skewed), the data are highly skewed.
why use non parametric test
area of study is better represented by the median When distribution skewed enough, the mean strongly affected by changes far out in the distribution's tail whereas the median continues to more closely reflect the centre of the distribution. small sample size
Measure of Central Tendency
measure of Central Tendency • Mean, Median, Mode
kurtosis
measures the degree of tailedness in the distribution
Interval scale
numeric scales in which we know the order and also the exact differences between the values.
why use parametric
perform well with skewed and non-normal distributions usually have more statistical power than nonparametric tests. Thus, you are more likely to detect a significant effect when one truly exists.