Inferential statistics help us draw conclusions about how a hypothesis will play out or to determine a general parameter about a larger sample. We often use this process to compare two groups of subjects to make greater generalizations about a larger overall population.
Inferential Statistics vs. Descriptive Statistics
What Are Inferential Statistics Used For?
Inferential statistics are generally used in two ways: to set parameters about a group and then create hypotheses about how data will perform when scaled.
Inferential statistics are among the most useful tools for making educated predictions about how a set of data will scale when applied to a larger population of subjects. These statistics help set a benchmark for hypothesis testing, as well as a general idea of where specific parameters will land when scaled to a larger data set, such as the larger set’s mean.
This process can determine a population’s z-score (where a subject will land on a bell curve) and set data up for further testing.
What’s the Difference Between Descriptive and Inferential Statistics?
Descriptive statistics are meant to illustrate data exactly as it is presented, meaning no predictions or generalizations should be used in the presentation of this data. More detailed descriptive statistics will present factors like the mean of a sample, the standard deviation of a sample or describe the sample’s probability shape.
Inferential statistics, on the other hand, rely on the use of generalizations based on data acquired from subjects. These statistics use the same sample of data as descriptive statistics, but exist to make assumptions about how a larger group of subjects will perform based on the performance of the existing subjects, with scalability factors to account for variations in larger groups.
Inferential statistics essentially do one of two things: estimate a population’s parameter, such as the mean or average, or set a hypothesis for further analysis.
What Is an Example of Inferential Statistics?
Any situation where data is extracted from a group of subjects and then used to make inferences about a larger group is an example of inferential statistics at work.
Though data sets may have a tendency to become large and have many variables, inferential statistics do not have to be complicated equations. For example, if you poll 100 people on whether or not they enjoy coffee, and 85 of those 100 people answer yes, while 15 answer no, the data will show that 85 percent of the sample enjoy coffee. Using that data, you might then infer that 85 percent of the general population enjoy coffee, while 15 percent of people do not.
