Descriptive and inferential statistics

It is subdivided into Descriptive statistics and inferential statistics.

Descriptive Statistics – Descriptive statistics is the term given to the analysis of data that helps describe, show or summarize data in a meaningful way such that, for example, patterns might emerge from the data. Descriptive statistics do not, however, allow us to make conclusions beyond the data we have analyzed or reach conclusions regarding any hypotheses we might have made. They are simply a way to describe our data.

For example, if we had the results of 100 pieces of students’ coursework, we may be interested in the overall performance of those students. We would also be interested in the distribution or spread of the marks. Descriptive statistics allow us to do this. How to properly describe data through statistics and graphs

Types of Descriptive Statistics:

1. Measures of Frequency:
2. Measures of Central Tendency
3. Measures of Dispersion or Variation
4. Measures of Position

Inferential statistics –

The branch of statistics concerned with drawing conclusions about a population from a sample. This is generally done through random sampling, followed by inferences made about central tendency, or any of a number of other aspects of a distribution. Inferential statistics use a random sample of data taken from a population to describe and make inferences about the population. Inferential statistics are valuable when examination of each member of an entire population is not convenient or possible.

For example, to measure the diameter of each nail that is manufactured in a mill is impractical. You can measure the diameters of a representative random sample of nails. You can use the information from the sample to make generalizations about the diameters of all of the nails.

1. Average marks obtained by all the students.
2. Grades or percentile of the scores.
3. Average score in cricket.
4. Prediction by a dentist about the teeth those are susceptible to have cavity or damage in future.

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

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, and 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.