Statistics play an important role in research in today’s growing world, helping collect, analyze and present the data in a measurable manner. As people usually do not know these two fields, it is difficult to identify whether the research is based on descriptive statistics or inferential statistics. The descriptive statistics describe the population, as the name suggests.
On the other side, Inferential statistics are used to make generalizations about the population based on samples.
There is a significant difference between descriptive and inferential statistics, e.g., what you do with your data. Let’s take a look at this article to learn more about the two topics.
What is statistics?
It could seem stupid to describe a “basic” concept as statistics in a data analysis blog. But it’s easy to take to it for granted if we frequently use those terms.
Statistics is the branch of applied mathematics concerned with data collection, organization, analysis, interpretation, and presentation. When using the term “Data Analytics,” we mean “statistical analysis of a particular dataset or datasets.” But it’s a little glitchy, so we tend to shorten it!
Since statistics are so essential for data analysis, they are also important in any field in which data analysts work. The wide range of statistical techniques available, from science and psychology to marketing and medicine, can be broadly divided into descriptive statistics and inferential statistics. But what is the difference between them?
What is descriptive statistics?
Descriptive statistics are used to describe the properties or features of a dataset. Descriptive statistics can explain the whole process of getting insights from each of these quantitative Observations.
To accurately represent data, the researchers sum up the data usage using numerical and graphical tools such as diagrams, tables, and graphs. Also, to explain what they represent, the text is presented in support of the charts.
Types of Descriptive Statistics –
- The measure of Central Tendency
- Measure of Variability
Following statistical measures are frequently used to describe groups in descriptive statistics:
Dispersion: How far away is the data from the center? To measure distribution, you can use the range or standard deviation. A small scattering means that the values cluster around the center more closely. Higher dispersion means that data points are farther from the center. The frequency distribution can also be graphically represented.
Skewness: The measure indicates the symmetric or skewed distribution of the values.
Central tendency: To locate the center of the data set, use the median or median. This measure shows where most of the values come from.
You may use numbers and charts to show this overview detail. It is typical descriptive statistics, but you can carry out other descriptive tests, including evaluating the relationships of paired data with correlations and dispersions.
What is inferential statistics?
Inferential statistics are all about the population generalization from the sample. Since the focus of inferential statistics is on prediction (not on factual information), its results are generally in probability form.
The most significant inferential statistics are based on statistical models like variance analysis, Chi-square analysis, t-distribution students, analysis of regression, and so on.
You can measure inferential statistics in a variety of ways, including:
Hypothesis tests: Hypothesis tests determine whether your population is worth more than a data point in your analysis. It can also conclude if people differ, which is based on the results of several experiments.
Confidence intervals: Confidence intervals Determine the margin of error in your research and whether or not it affects what you’re testing for. For mean and median calculations, you’ll primarily need to estimate the range of a population’s possible values.
Regression analysis: A regression analysis is a relationship between an experiment’s independent and dependent variables. After you know the hypothesis test results, you can perform a regression analysis to determine the relationship of the subject matter. You can test for things like the difference in height and weight between two populations or the height and weight of different genders.
In brief: what is the difference between descriptive and inferential statistics?
We have studied the differences between descriptive and inferential statistics in this article. Let’s see what we have learned.
- Describe the population and sample characteristics.
- Data should be organized and presented factually.
- Use tables, charts, or graphs to give the final results visually.
- Draw conclusions based on available data.
- Make use of measures such as central tendency, distribution, and variance.
- Make generalizations about larger populations based on samples.
- Present final results in probability form.
- Draw conclusions based on information that is not generally available.
- Make use of techniques such as hypothesis testing, confidence intervals, regression, and correlation analysis.
There’s enough discussion about the two topics. All you need to know is that descriptive statistics illustrate your current data, while inferential statistics concentrate on making hypotheses of the different populations beyond the study dataset. While descriptive statistics provide enough analysis of the researchers’ data, inferential statistics generalize the data, meaning that the provided data is not studied.