# Inferential Statistics – Definition & Overview

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There are two main branches of statistics- Descriptive Statistics and Inferential Statistics. As the name suggests descriptive statistics is all about describing the data – by calculating quantities like mean, median, mode quartile, standard deviation, etc we get an idea about how our data looks like.

Drawing of various charts and graphs like pie charts, bar graphs, histograms, ogives, etc. also belongs to the area of descriptive statistics.

Inferential statistics on the other hand is about using the given data to make conclusions (“inferences”) about the parent population.

For example, to find the average height of all Americans we might take a random sample of Americans and apply the techniques of inferential statistics to estimate the mean height of all Americans.

Inferential Statistics consists of two parts-

Hypothesis testing –

Hypothesis testing involves checking whether some statement makes about a population is true or not. For example, we might wish to test the claim of a manufacturer that the bulbs produced in his factory have a mean life of 5 months.

We test these kinds of hypotheses by drawing a random sample from the population. Some basic examples of statistical tests are the T-test, F-test, ANOVA, Goodness of Fit test, etc.

Estimation –

This involves estimating the value of an unknown population parameter. Point estimate gives us a single value as an Estimate for the population parameter. Interval estimate involves the calculation of various confidence intervals within which parameter values are expected to lie.