What is the difference between variance and variability?

The terms variance and variability both describe the spread or dispersion of data, but they are used in slightly different contexts and have distinct meanings.

Variance:

Variance is a specific statistical measure of the dispersion of a set of values.

It is the average of the squared differences from the mean.

The formula for variance (σ²) of a dataset is:

Variance = Σ (xᵢ - μ)² / N

where:

xᵢ is each individual data point,
μ is the mean of the dataset,
N is the total number of data points in the dataset.
Variance is expressed in the square of the units of the original data, which is why it can sometimes be difficult to interpret directly.

It is often used in statistical analysis to quantify how data points differ from the mean.

Variability:

Variability is a more general term that refers to how spread out or varied a set of data is.
It can be assessed using various methods, such as range, standard deviation, and variance.
Unlike variance, variability doesn't refer to a specific measure or calculation. It is a broader concept that describes the extent to which data points differ from each other.
Key Difference:

Variance is a specific numerical measure (based on squared deviations) used to quantify the degree of variability in a dataset, whereas variability is a general concept describing the spread or diversity of data, which can be measured in various ways.