Statistics – Measures:

In statistics we deal with huge amounts of data related to a particular survey or experiment. We cannot pin locate and analyze the data for future predictions based on each value. The bulkiness of the data can be reduced by organizing it into a frequency table or histogram. Frequency distribution organizes the heap of data into a few meaningful categories. Collected data can also be summarized as a single index/value, which represents the entire data. These measures may also help in the comparison of data. There are three types of measures in statistics based upon the type of data.

Measure of central tendency

Measure of spread/variability/dispersion

Measure of shape

The statistical measure which helps to identify an entire distribution by a single value is known as Central tendency. It is often used as an accurate description of the data. It is the single value that is most typical/representative of the collected data. The three commonly used measures of central tendency are mean, median and mode.

Variability also referred as dispersion/Spread, measures whether the data values are tightly clustered or spread out and how much they differ from the mean value. The spread of the values can be measured for numeric variables (quantitative data) arranged in ascending order.  Measures of the spread like variance and standard deviation of the data are present near the mean. If the data set spreads large, the mean is not as representative of the data as when the spread of data is small. This is because when there are large differences among individual scores it indicates larger spread.

The two numerical measures of shape skewness and kurtosis can be used to test for normality.

The data set is not normally distributed when these values are not close to zero.

Let’s get into detailed versions.