Descriptive Statistics

Descriptive statistics are characteristics that seek to represent the data set to which they belong to, from a quantitative perspective; they are statistics that describe your data. They are important to statistics because they give both context about the attributes, as well as uncover relationships between them, that may not have been as obvious before. In descriptive statistics, four different types are used to measure characteristics within a dataset:

1. Measures of Variation or Dispersion - helps recognized the size variance between data points within the set. Techniques like standard deviation, range, and variance are used.
2. Measure of Position - used to compare data points in relation to each other, using a normalized score. Techniques like percentile ranks and quartile ranks are used.
3. Measures of Central Tendency - helps recognize averages or recurring values within a dataset. Uses methods like mean, median, and mode.
4. Measures of Frequency - helps recognize the recurrence of an attribute on a graph when all values are visible and plotted. This is generally represented as a percentage.