Descriptive Statistics Vs Inferential Statistics- 8 Differences

What is Statistics?

  • Statistics refers to the practices used for collecting, organizing, analyzing, and interpreting data to acquire knowledge on a specific topic or research and sketch assumption based on the data.

What are the types of Statistics?

The two main branches of Statistics are

  1. Descriptive Statistics
  2. Inferential Statistics

1. Descriptive statistics:

  • Descriptive Statistics is a branch of statistics that indicates to provide information and define the data briefly.
  • Descriptive Statistics refers to a discipline that quantitatively describes the data.
  • Descriptive statistics describe only about particular people or items that are measured.
  • It is applicable widely for small projects but the results cannot extrapolated to a large population.
  • In descriptive statistics, we can take a sample according to our interest and analyze data to present its properties.
  • Descriptive statistics is used to describe particular situation. It gives details of the data that is recognized and summarizes the data of the sample.
  • Descriptive statistics mostly use statistical measures like Central tendency, dispersion, and skewness.
    • Central tendency: Use the mean and median to analyze the center of the data set.
    • Dispersion: How far out from the center do the data extend? You can use the range or standard deviation to measure the dispersion. A low dispersion indicates that the values cluster more tightly around the center. Higher dispersion signifies that data points fall further away from the center. We can also graph the frequency distribution.
    • Skewness: It helps to measure the values of distribution whether it is symmetric or skewed.
  • The form of final result are charts, graphs and tables.
  • Descriptive statistics organizes, analyzes and presents data in a meaningful way.

2. Inferential statistics:

  • Inferential Statistics is a type of statistics that define data of a larger population by taking a small portion of that population and draws a conclusion from it.
  • Inferential Statistics refers to a discipline that provides information and draws the conclusion of a large population from the sample of it.
  • Inferential statistics describe data about the population entirely.
  • It is more applicable for larger data set projects.
  • In Inferential statistics, a sample is done through different forms of sampling.
  • Inferential statistics is used to clarify the probabilities of occurrence of an event. It attempts to reach the conclusion to learn about the population that extends beyond the available data.
  • Common statistical tools of inferential statistics are: hypothesis Tests, confidence intervals, and regression analysis.
    • Hypothesis tests: It helps in the prediction of the data results and answers questions like the following:
      • Is the population mean greater than or less than a specific value?
      • Are the means of populations are identical or diverse from each other?
    • Confidence intervals:
      • It helps in the estimation of a population parameter.
      • It includes the improbability and sample error to create a range of value and gives a direction where the data mean falls in the given range during the analysis.
    • Regression analysis: Regression analysis defines the relationship between two variables i.e. independent variable and dependent variable. The analysis also includes a hypothesis test to determine the probabilities of relationships in sample data of the total population.
  • The form of final results of inferential statistics is probability.
  • Inferential statistics compares, tests and predicts data.

8 Differences Between Differential and Inferential Statistics:

Differences based on Descriptive statistics Inferential statistics.
Meaning Descriptive Statistics refers to a discipline that quantitatively describes the data. Inferential Statistics refers to a discipline that provides information and draws the conclusion of a large population from the sample of it.
Description Descriptive statistics describe only about particular people or items that are measured. Inferential statistics describe data about the population entirely.
Application It is applicable widely for small projects but the results cannot extrapolated to a large population. It is more applicable for larger data set projects.
The Choice In descriptive statistics, we can take a sample according to our interest and analyze data to present its properties. In Inferential statistics, a sample is done through different forms of sampling.
Function To describe particular situation.

 

It gives details of the data that is recognized and summarizes the data of the sample.

To clarify the probabilities of occurrence of an event.

 

It attempts to reach the conclusion to learn about the population that extends beyond the available data.

 

 

Tools Descriptive statistics mostly use following statistical measures i.e.

Central tendency, dispersion, and skewness.

Central tendency: Use the mean and median to analyze the center of the data set.

 

Dispersion: How far out from the center do the data extend? You can use the range or standard deviation to measure the dispersion. A low dispersion indicates that the values cluster more tightly around the center. Higher dispersion signifies that data points fall further away from the center. We can also graph the frequency distribution.

 

Skewness: It helps to measure the values of distribution whether it is symmetric or skewed.

 

Inferential statistics common statistical tools are: Hypothesis Tests, confidence intervals, and regression analysis.

Hypothesis tests: It helps in the prediction of the data results and answers questions like the following:

  • Is the population mean greater than or less than a specific value?
  •  Are the means of populations are identical or diverse from each other?

Confidence intervals:

  • It helps in the estimation of a population parameter.
  •  It includes the improbability and sample error to create a range of value and gives a direction where the data mean falls in the given range during the analysis.

Regression analysis:

  • Regression analysis defines the relationship between two variables i.e. independent variable and dependent variable. The analysis also includes a hypothesis test to determine the probabilities of relationships in sample data of the total population.

 

 

Form of final result Charts, Graphs and Tables Probability
What does it do? Organize, analyze and present data in a meaningful way. Compares, test and predicts data.

References and For More Information:

https://blog.minitab.com/blog/understanding-statistics/understanding-qualitative-quantitative-attribute-discrete-and-continuous-data-types

https://study.com/academy/lesson/descriptive-and-inferential-statistics.html

https://statisticsbyjim.com/basics/descriptive-inferential-statistics/

https://keydifferences.com/difference-between-descriptive-and-inferential-statistics.html

About Kusum Wagle 214 Articles
Hello and greetings everyone! I am Kusum Wagle, MPH, WHO-TDR Scholar, BRAC James P. Grant School of Public Health, Bangladesh. I have gained profound experiences in public health sector under different thematic areas of health, nutrition, sexual and reproductive health, maternal and newborn health, research etc., targeting diverse audience of different age groups. I have performed diverse roles ranging from lecturer in the public health department of colleges, nutrition coordinator, research coordinator and consultant, in different programs, projects and academic institutions of Nepal. I also hold immense experience in working closely and persistently with government organizations, non-government organizations, UN agencies, CSOs and other stakeholders at the national and sub-national level. I have successfully led and coordinated different projects involving multi-sector participation and engagement. Moreover, I am also regularly involved in the development of different national health related programs and its guidelines.