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Descriptive and Inferential Statistics

Statistics is the study of numerical data. It deals with the gathering, presentation, management, organization, calculation and analysis of usually vast numerical data.

There are majorly two kinds of divisions of statistics:

Descriptive Statistics

Inferential Statistics

The statistical analysis enables us to draw conclusions about several different statistical situations, both in descriptive and inferential statistics. Both the segments are equally important. Both have different objectives.

Basically, the descriptive statistics describes the features of the sample data quantitatively. On the other hand, the inferential statistics does inferences for the population data from which the given samples were taken.

Let us first discuss about the two most basic concepts: population and sample. The population is defined as the whole set of data, individuals, events or objects etc on which the researcher is performing research.

The whole area of study is included in a population. While, the sample is relatively smaller. It is a subset of the population. Since it is difficult to handle and analyze each and every member in the population, a smaller and representative portion from the population is picked up. This is called sample.

Usually, both descriptive and inferential statistics are used in most of the statistical researches in order to properly analyze the data and reach conclusions.

Both types provide different insights about the nature of given data. They together make a powerful tool for the better analysis of the data. But what is the difference between these two types of statistics? Let us go ahead and learn about both of them in detail.

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Descriptive Statistics Definition

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The descriptive statistics is the type of statistical analysis which helps to describes about the data in some meaningful way. This statistics is used to describe quantitatively about the important features of the data or information. The descriptive statistics gives the summaries of the given sample as well as the observations done. These summaries or descriptions can either be graphical or quantitative.

For Example: In soccer, the individual performance of each player is said to be the a descriptive statistics.
However, descriptive statistics does not reach at conclusions beyond the given data or hypothesis made by the researcher. It is just a simple way of describing the data.

Generally, the kinds of measure that are used with descriptive statistics are:

1) Measures of Central Tendency:
The measure of central tendency describes the data which lies in the center of a given frequency distribution. The main measures of central tendency are mean and median and mode.

2) Measures of Spread:
The measure of spread describes the how the scores are spread over the whole distribution. Standard deviation, variance, quartiles, range, absolute deviation are included in the measures of spread.

3) Graphical Representation:
There are several different types of graphs that are used to describe about the statistical data. These graphs are histogram, bar graph, box and whisker plot, line graph, scatter plot, ogive, pie chart and many more.

Inferential Statistics Definition

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Inferential statistics is the type of statistics which deals with making conclusions. It inferences about the predictions for the population. It also analyses the sample. Basically, the inferential statistics is the procedure of drawing predictions and conclusions about the given data which is subjected to the random variations. Inferential statistics includes detection and prediction of observational and sampling errors. This type of statistics is being utilized in order to make estimates and test the hypotheses using given data. 

The inferential statistics may be defined as the answer of the question "what is needed to be done next". This provides an information about the further surveys and experiments. Inferential statistics enables the researcher to draw conclusions before the implementation of some particular organizational policy.
There are two major divisions of inferential statistics:

1) Confidence Interval:
The confidence interval is represented in the form of an interval that provides a range for the parameter of given population.

2) Hypothesis Test:
Hypothesis tests are also known as tests of significance which tests some claim for the population by analyzing sample.

Difference Between Descriptive and Inferential Statistics

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Although descriptive and inferential statistics both are used for purpose of analysis of the data, still both of them are different in various ways. Let us learn about this difference below:

1) The descriptive statistics gives a description about a sample, while the inferential statistics predicts and infers about a much larger data or population.

2) Descriptive statistics just describes the certain characteristics about a data. Whereas, inferential statistics deeply analyzes the statistical data and observations.

3) Descriptive statistics deals with central tendency and spread of the frequency distribution. While in inferential statistics, more details such as hypothesis tests and confidence interval are studied.

4) The measures of descriptive statistics (mean, median, mode) are numbers. On the other hand, the measures in inferential statistics are not always exact numbers.

5) Descriptive statistics deals with small samples which enables us to produce results without errors. But inferential statistics takes whole population for drawing conclusions which may not have the extent of required accuracy .

6) In descriptive statistics, the conclusions cannot be made beyond the given data. In inferential statistics, the educated predictions and guesses can be made on the basis of the parameters of the given population, it does not matter how big the population is.

Examples

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Few common examples of descriptive and inferential statistics are given below.

Examples of Descriptive Statistics:
i) Estimation of number of students (boys and girls separately) in a school.
ii) Population of particular county or city.
iii) Frequency of the variables.
iv) Estimation of number of damaged or cavity teeth by a dentist.

Examples of Inferential Statistics:
i) Average marks obtained by all the students.
ii) Grades or percentile of the scores.
iii) Average score in cricket.
iv) Prediction by a dentist about the teeth that are susceptible to have cavity or damage in future.
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