How would you describe central tendency and variability?

While central tendency tells you where most of your data points lie, variability summarizes how far apart your points from each other. Data sets can have the same central tendency but different levels of variability or vice versa. Together, they give you a complete picture of your data.

What is an example of variability?

Variability refers to how spread scores are in a distribution out; that is, it refers to the amount of spread of the scores around the mean. For example, distributions with the same mean can have different amounts of variability or dispersion.

How are measures of central tendency and variability related?

Measures of central tendency are statistics or numbers expressing the most typical or representative scores in a data distribution. Measures of variability are statistics representing the extent to which scores are dispersed (or spread out) numerically.

Which is more sensitive to variability, the median or the mean?

The mean takes into account the value of every observation and thus provides the most information of any measure of central tendency. Unlike the median, however, the mean is sensitive to outliers. In other words, one extraordinarily high (or low) value in your dataset can dramatically raise (or lower) the mean.

Where is the central tendency of a histogram?

In a negatively skewed distribution, there’s a cluster of higher scores and a spread out tail on the left. In this histogram, your distribution is skewed to the right, and the central tendency of your data set is on the lower end of possible scores.

How to use measures of center and variability?

Videos and lessons with examples and solutions to help High School students learn how to use measures of center and measures of variability for numerical data from random samples to draw informal comparative inferences about two populations.