Difference Between Descriptive Statistics and Inferential Statistics


Main Difference

The main difference between Descriptive Statistics and inferential Statistics is that Descriptive Statistics utilize the data to provide depictions of the population, either through numerical calculations or graphs or tables and Inferential Statistics makes conclusions and predictions about a population based on a sample of data taken from the population in question.

Descriptive Statistics vs. Inferential Statistics

Descriptive statistics is one which characterizes the population. On the other end, Inferential statistics are used to generalize the population based on the samples. Descriptive statistics is the term provided to the examination of data that helps to summarize or show data in a meaningful manner. Inferential Statistics called sampling is used to make sure the sample chosen represents the population as closely as possible. In descriptive statistics, data summarized and represented in an accurate way using charts, tables, and graphs whereas inferential Statistics determines the probability of the characteristics of the sample using probability theory. In descriptive statistics, tools are used to measure of central tendency (mean/median/mode), the spread of data (range, standard deviation, etc.), and in inferential Statistics, tools used for hypothesis tests, analysis of variance, etc.


Comparison Chart

Descriptive StatisticsInferential Statistics
Descriptive Statistics is that section of statistics which is involved with describing the population under study.Inferential Statistics is a kind of statistics, that emphasis on concluding the population, by sample analysis and observation.
To define a situation.To describe the chances of occurrence of an event.
What does it do?
Set up, analyze and present data in a meaningful way.Equates, test and predicts data.
It describes the data, which is already known, to summarize sample.It tries to reach a conclusion to learn about the population, that extends beyond the data available.
A form of Final Result
Charts, Graphs, and TablesProbability

What is Descriptive Statistics?

Descriptive statistics is the term of statistics given to the survey of data that helps describe, show or abstract data in a significant extent. Descriptive statistics are very significant because if we present our raw data, it would be hard to imagine what the data was showing, especially if there was a lot of it. Descriptive statistics, therefore, allows us to present the data in a more meaningful way, which allows simpler interpretation of the data. Descriptive statistics describe data through statistics and graphs is an important topic and discussed in other agrarian Statistics guides.


  • Measures of central tendency: these are means of defining the central position of a frequency distribution for a group of data. In this instance, the frequency distribution is simply the distribution and standard of marks accounted by the 100 students upon the lowest to the highest. We can explain this central position using some statistics, containing the mode, median, and mean. You can read about measures of a central tendency here.
  • Measures of spread: these are means of summarizing a group of data by describing how to spread out the scores are. Measures of spread help us to shorten how spread out these scores is. To describe this spread, some statistics are available to us, including the range, quadrilles, absolute deviation, variance, and standard deviation.

What is Inferential Statistics?

Inferential statistics, unlike descriptive statistics, is the effort to apply the conclusions obtained from one experimental study to more general populations. Inferential statistics attempts to answer questions about populations and samples that not tested in the given experiment. If you carry out a survey, the goal is to apply the completion to a more general population, assuming the sampling extent is large enough and the sample representative enough of the broader public. Inferential statistics is important because studies and experiments need to declare and conclude something about comprehensive populations and not just over the sample that was studied. Inferential statistics are valuable when examination of each member of an entire population is not convenient or possible. Inferential statistics use statistical patterns to help you compare your sample data to other samples or previous research.


  • Estimating parameters. This means getting a statistic from your sample data (for example the sample mean) and utilizing it to say something about a population constant (i.e., expected value).
  • Hypothesis tests. This is where you can utilize sample data to respond to research questions. For example, you might be curious in knowing if a new cancer drug is effective. Or if breakfast helps children perform better in schools.

Key Differences

  1. Descriptive Statistics is an order which is involved with describing the population under study. Inferential Statistics is a type of statistics; that focuses on concluding the population, by sample analysis and observation.
  2. There is a graphic or tabular representation of the final result in descriptive statistics whereas the final result displayed in the form of probability.
  3. Descriptive statistics describe the data, which is already known, to summarize sample. Conversely, inferential statistics attempts to conclude to learn about the population; that extends beyond the data available.
  4. Descriptive Statistics collects, organizes, analyzes and presents data in a meaningful way. On the contrary, Inferential Statistics contrasts data, test hypothesis and make predictions of the future outcomes.
  5. Descriptive statistics describe a condition while inferential statistics explains the likelihood of the occurrence of an event.


Therefore, we have an adequate discussion on the two terms, all you require to know is that descriptive statistics is all about representing your existing set of data whereas inferential statistics concentrate on making a conjecture on the additional population, that is above the set of data under study. While descriptive statistics provide the totality of the data, the researcher has studied whereas inferential statistics, makes the induction, which means the data provided to you is not studied.