2F: Centre and Spread for Symmetric Distributions

*with no outliers

Centre: Mean or Median

Spread: Standard Deviation

Mean = (sum of all values) / (number of values)

We know that the mean (average) value of a data set is the sum of the values/number of values.

But what does this tell us?

The mean is the balance point of a distribution

https://www.smartpropertyinvestment.com.au/research/13198-investors-misled-by-skewed-median-prices

https://www.jimsparrow.com/blog/average-or-median-sale-price.html

The mean is not appropriate for determining the centre of the data for skewed distributions. Why?

Mean is significantly affected by extreme values. The median is a better measure of centre as it is relatively unaffected by the presence of extreme values.

To measure the spread of a data distribution around the median (M) we use the interquartile

range (IQR). To measure the spread of a data distribution about the mean ( ¯x) we use the

standard deviation (s).