Difference Between Descriptive and Inferential Statistics

Two critical types of statistical methods are used widely. One is descriptive statistics, and the other is inferential statistics.


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These two combined offer a lot of advantages in identifying future needs. Statistics stand based on many informed decisions by a government or an organization.

Descriptive Statistics and Inferential Statistics help conclude many issues that must be addressed. The best part about statistics is it addresses future needs.

Key Takeaways

  1. Descriptive statistics summarize and describe the main features of a dataset, while inferential statistics use sample data to make predictions or draw conclusions about a population.
  2. Descriptive statistics include central tendency and dispersion measures, while inferential statistics involve hypothesis testing and estimation techniques.
  3. Descriptive statistics provide a foundation for data analysis, while inferential statistics allow researchers to make data-driven decisions and predictions.

Descriptive vs Inferential Statistics

Descriptive statistics summarizes and describes the main features of a dataset, such as the mean, median, and standard deviation. It provides a way to understand the distribution and pattern of data. Inferential statistics uses a sample of data to make inferences about the population from which the data was drawn.

Descriptive vs Inferential Statistics

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Comparison Table

Parameter of ComparisonDescriptiveInferential
Meaning/DefinitionDescriptive Statistics is the branch of statistics that describes the population.Inferential Statistics is the branch of statistics that concludes the entire population by studying a sample percentage of people.
Nature of WorkDescriptive statistics organizes, analyses, and presents data in a significant way for more investigation.Inferential statistics is about comparing data and predicting from the analyzed data to conclude.
Final ResultDescriptive statistics offers graphs, Charts, and also tables.Inferential Statistics gives the probability of a particular occurrence inferred from the data.
Main UsageDescriptive statistics describe the situation understudy.Inferential statistics clearly explains the future occurrence of an event by chance.
Main FunctionalityDescriptive statistics gives data which everyone knows. It just summarizes it.Inferential statistics reaches beyond the data. It gives conclusions about the population under study and helps in learning the population’s behaviour percentage-wise.


What is Descriptive Statistics?

Descriptive statistics is the branch that helps describe the population under study. Descriptive statistics quantitatively describes the essential characteristics of the dataset.

The description happens through specific properties like mean, median, mode, and also measures of dispersion. Descriptive statistics provide information in a meaningful way by utilizing graphs, charts, and tables.

The data is mentioned accurately too. The information may also contain a few diagrams, which will be explained in the same context.

Descriptive statistics offer simple information about the sample in the study. This forms the first phase of data analysis for a substantial statistical analysis.

Descriptive statistics are extensively used in the business world to procure valuable data.

The dataset from which the statistical analysis is carried out fetches a lot of information already known to everyone. Still, it is presented in a meaningful impact it created on a specific situation.

At times, the sample dataset may have two to three variables. In that case, descriptive statistics are bound to give the relationship among all three variables.

There are three types of analysis; univariate, bivariate, and multivariate.

descriptive statistics

What is Inferential Statistics?

Inferential statistics is the branch of statistics that concludes by analyzing a sample from a whole lot of a particular pattern. Inferential statistics is generalizing a specific fact to the entire lot by examining a model.

The deduction of the result from the sample is judged the same for the whole group. It is a very convenient way when a large number of numbers or populations cannot be examined for a particular cause.

The sample chosen must be precisely from the whole lot, and the result of the model will directly apply to the entire lot. Primarily, inferential statistics work with probability theory.

The methods used in inferential statistics are estimating parameters and testing the hypothesis. The propositions from the sample become models, and the same model is subjected to the entire community.

There are many models of inferential statistics; it involves the approximate distribution of the sample data. But all the models arrive at one particular conclusion but are used in different scenarios.

Inferential analysis can be used to check what the entire population might think about the new government. This can happen just by checking with a few thousand people.

inferential statistics

Main Differences Between Descriptive and Inferential Statistics

  1. Descriptive statistics describe the population, while inferential statistics help learn the population by examining a sample. It helps in concluding by analyzing the data of a piece of people.
  2. Descriptive statistics is brilliant in providing the data in a meaningful way through graphical representations, charts, and tables, while inferential statistics help in comparing and predicting from the data.
  3. The final result of descriptive statistics can be a diagram or a graphical representation, but inferential statistics offer the probability of a particular occurrence.
  4. Descriptive statistics help in describing a situation, while inferential statistics explain the chance of a future event occurrence.
  5. Descriptive statistics give the already known data, but inferential statistics conclude learning about the population. This is sometimes beyond the numbers projected.
Difference Between Descriptive and Inferential Statistics
  1. https://repository.upenn.edu/cgi/viewcontent.cgi?article=1314&context=marketing_papers
  2. https://journals.library.ualberta.ca/eblip/index.php/EBLIP/article/view/168
  3. https://psycnet.apa.org/record/1994-98130-000
  4. https://arxiv.org/abs/1302.2525
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