8 Difference Between Descriptive And Inferential Statistics (With Examples)

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Descriptive Statistics uses data to provide description of a population either through numerical calculations or graphs or tables. Inferential statistics on the hand makes inferences and prediction about a population based on a sample of data taken from the population in question. Below are more insights into how descriptive statistics differ from inferential statistics.

The Difference

Definition

Descriptive statistics is a branch of statistics that focuses on summarizing the data collected from a sample. The technique produces measures of central tendency and dispersion which represent how the values of the variables are concentrated and dispersed. In contrast, inferential statistics generalizes the statistics obtained from a sample to the general population to which the sample belongs. The measures of the population are termed as parameters.

Application

Descriptive statistics are limited because the statistics can only be applied to the data you have actually measured. They cannot be extrapolated to other groups of data. On the other hand, inferential statistics can be applied to a larger population of data as long as the sample data which is used as representative of the population.

Accuracy

Descriptive statistics are likely to be 100% accurate because there are no assumptions being made about the raw data that is used. In contrast, inferential statistics always make inferences about a large population based on a smaller sample. This method cannot be lacking errors. This gives descriptive statistics the edge over inferential statistics.

Methods

Methods used in descriptive statistics are Measure of central tendency (Use of a single value is relied upon to describe data) and Measures of dispersion (use of average data). In contrast, inferential statistics uses the following methods to make conclusions: confidence interval and Hypothesis testing.  Confidence interval measures one sample and gives a range of values for an unknown population parameter. Hypothesis testing is an assumed analysis of a sample.

Graphical methodologies used

In descriptive statistics graphical methodologies used are pie charts, Bar graphs, histograms, frequency distribution chart and mean analysis graphs. On the other hand, inferential statistics employ graphical methodologies such as correlation analysis, survival analysis, linear regression graph, ANOVA, structural equation modeling.

Use of Probability

Descriptive statistics does not require use of probabilities (assumptions) on the part of the user as all of the raw data is already there whereas inferential statistics requires the user to make assumptions or guesses before running the test.

Properties of the sample

The properties of the population in descriptive statistics such as mode, mean etc are referred to as parameters. Contrary, properties of the samples in inferential statistics are not referred to as parameters; instead they are just referred to as statistics.

Example in Everyday Life

A good example of the use of descriptive statistics is calculating Grade Point Average (GPA) of a student. The Grade Point Average in essence is the weighted mean of the students’ results and is a reflection of the overall academic performance of that particular student. On the other hand, a good example of inferential statistics in action is the prediction of the results of an election prior to the voting by means of polling.

Also Read: Difference Between Dependent And Independent Variables

What are some of the differences between descriptive and inferential statistics in a comparison Chart?

Basis of Comparison Descriptive Statistics Inferential Statistics
Definition Descriptive statistics is a branch of statistics that focuses on summarizing the data collected from a sample. Inferential statistics generalizes the statistics obtained from a sample to the general population to which the sample belongs.
Application Application is limited because the statistics can only be applied to the data you have actually measured. Can be applied to a larger population of data as long as the sample data which is used as representative of the population.
Accuracy Statistics are likely to be 100% accurate because there are no assumptions being made about the raw data that is used. Statistics always make inferences about a large population based on a smaller sample. This method cannot be lacking errors.
Methods Methods used in descriptive statistics are Measure of central tendency (Use of a single value is relied upon to describe data) and Measures of dispersion (use of average data). Inferential statistics uses the following methods to make conclusions: confidence interval and Hypothesis testing. 
Graphical Methodologies Used Graphical methodologies used are pie charts, Bar graphs, histograms, frequency distribution chart and mean analysis graphs. Employ graphical methodologies such as correlation analysis, survival analysis, linear regression graph, ANOVA, structural equation modeling.  
Use of Probability Does not require use of probabilities (assumptions) on the part of the user as all of the raw data is already there Requires the user to make assumptions or guesses before running the test.  
Properties of the Sample The properties of the population in descriptive statistics such as mode, mean etc are referred to as parameters. Properties of the samples in inferential statistics are not referred to as parameters; instead they are just referred to as statistics.
Example in Everyday Life 3 A good example of the use of descriptive statistics is calculating Grade Point Average (GPA) of a student. The Grade Point Average in essence is the weighted mean of the students’ results and is a reflection of the overall academic performance of that particular student. A good example of inferential statistics in action is the prediction of the results of an election prior to the voting by means of polling.  

Summary

What is the main difference between Descriptive and Inferential Statistics?

Descriptive statistics is a branch of statistics that focuses on summarizing the data collected from a sample. The technique produces measures of central tendency and dispersion which represent how the values of the variables are concentrated and dispersed. In contrast, inferential statistics generalizes the statistics obtained from a sample to the general population to which the sample belongs. The measures of the population are termed as parameters.