Descriptive and Inferential Statistics Differences, Examples

Descriptive statistics focuses on summarizing and presenting data to highlight its main features, while inferential statistics aims to make predictions and generalizations about a population based on sample data. Understanding and applying these two branches of statistics enables researchers, analysts, and engineers to make informed decisions, draw meaningful conclusions, and advance knowledge in their respective fields. Looking at how a sample set of rural patients responded to telehealth-based care may indicate it’s worth investing in such technology to increase telehealth service access. Techniques like hypothesis testing and confidence intervals can reveal whether certain inferences will hold up when applied across a larger population.

Difference between Descriptive and Inferential statistics

In a “symmetric” distribution, all three are the same, whereas in skewed data the median and mean are not the same; lie more toward the skew, with the mean lying further to the skew compared with the median. For example, in Figure 3, a right skewed distribution is seen (direction of skew is based on the tail); data values’ distribution is longer on the right-hand (positive) side than on the left-hand side. Understanding the differences between descriptive vs. inferential statistics is just one part of the puzzle. Knowing how and when to use each type is a completely separate challenge and one that many statisticians may struggle with. With inferential statistics, you wouldn’t just take a single chocolate from the box and then use what you learn from that to predict or generalize the rest of the box’s contents. Nor would use the data from your box to predict the types and varieties of hundreds of other chocolate boxes.

A student’s grade point average (GPA), for example, provides a good understanding of descriptive statistics. The idea of a GPA is that it takes data points from a range of individual course grades, and averages them together to provide a general understanding of a student’s overall academic performance. We have seen that descriptive statistics provide information about our immediate group of data.

Uses cases of Descriptive Statistics

1. The following methodologies that entail the types of descriptive statistics are used for summarizing the findings present within the data.
2. It implies that approximately 99.78% of the students have scored less than Avinashi.
3. We therefore sample from the population; ideally, we do so randomly, but there are other types of sampling methods available.
4. Another analysis was to examine pretest sensitisation, tested through a hypothesis that a group randomly assigned to receive a pretest and post-test would not be significantly different from a post-test-only group.
5. Also, based on this sample, we want to determine if we can predict whether the next new product will be defective.
6. 1,2 To put it in very simple terms, a variable is an entity whose value varies.

It is aimed at individuals new to research with less familiarity with statistics and may be helpful information when reading research or conducting peer review. Often, however, you do not have access to the whole population you are interested in investigating, but only a limited number of data instead. For example, you might be interested in the exam marks of all students in the UK. It is not feasible to measure all exam marks of all students in the whole of the UK so you have to measure a smaller sample of students (e.g., 100 students), which are used to represent the larger population of all UK students. Properties of samples, such as the mean or standard deviation, are not called parameters, but statistics.

Descriptive and Inferential Statistics

Ideally, group sizes should be equal (except in extreme ends where open groups are used; e.g., age “greater than” or “less than”). This descriptive vs. inferential statistics guide breaks down all the big differences between the two. It’s very difficult to summarize and conclude anything about the data without statistical analysis. The application of statistical analysis has its presence in almost all domains today, including finance & accounting, marketing, research, IT, supply chain, and economics. Regression analysis is used for quantifying the association between variables.

It provides methods for organizing, visualizing, and presenting data meaningfully and informally. Descriptive statistics describe the characteristics of the data set under study without generalizing beyond the analyzed data. Following up with inferential statistics can be an important step toward improving care delivery, safety, and patient experiences across wider populations. Since it’s virtually impossible to survey all patients who share certain characteristics, Inferential statistics are crucial in forming predictions or theories about a larger group descriptive vs inferential statistics of patients.

While the aforementioned statistics can be calculated manually, researchers typically use statistical software that process data, calculate statistics and p values, and supply a summary output from the analysis. However, the programs still require an informed researcher to run the correct analysis and interpret the output. Try using the programs through a demonstration or trial period before deciding which one to use. It also helps to know or have access to others using the program should you have questions. Measures of Central Tendency, Graphical Representation, Measures of Dispersion are some types of descriptive statistics.

When we reject a null hypothesis, when it is true, we label it as a Type I error (also denoted as “alpha,” correlating with significance levels). In a Type II error (also denoted as “beta”), we fail to reject a null hypothesis, when the alternate hypothesis is actually true. Type II errors are usually expressed as “1- β,” correlating with the power of the test.

In these cases, the investigators might have to rework outcomes or maybe pool data from multiple centers. Although post hoc power can be analyzed, a better approach suggested is to calculate 95% confidence intervals for the outcome and interpret the study results based on this. Descriptive statistics summarize and describe the features of a dataset, focusing on what the data shows. In contrast, inferential statistics use data from a sample to make predictions or inferences about a larger population. Do you want to gain an in-depth understanding of descriptive vs. inferential statistics? Do you want to master the computation of summary statistics and gain a thorough knowledge of both branches?

1. In recent years, the embrace of information technology in the health care field has significantly changed how medical professionals approach data collection and analysis.
2. IQR is the range for the middle 50% of the observations in the data as it is calculated after removing the highest and the lowest 25% of observations in a dataset after arranging them in ascending order.
3. Descriptive and inferential statistics have different tools that can be used to draw conclusions about the data.
4. Sample size calculation also needs to add corrections for patient drop-outs/lost-to-follow-up patients and missing records.
5. For a variable to be “good,” it needs to have some properties such as good reliability and validity, low bias, feasibility/practicality, low cost, objectivity, clarity, and acceptance.

Instead, they’re used as preliminary data, which can provide the foundation for future research by defining initial problems or identifying essential analyses in more complex investigations. However, inferential statistics methods could be applied to draw conclusions about how such side effects occur among patients taking this medication. The resulting inferential statistics can help doctors and patients understand the likelihood of experiencing a negative side effect, based on how many members of the sample population experienced it. Other than the clarity with which descriptive statistics can clarify large volumes of data, there are no uncertainties about the values you get (other than only measurement error, etc.). Visualizing data distributions effectively can be incredibly powerful, and this is done in several ways.

Detecting and managing outliers is a step in descriptive statistics to ensure accurate and reliable data analysis. To identify outliers, you can use graphical techniques (such as boxplots or scatter plots) or statistical methods (such as Z-score or IQR method). These approaches help pinpoint observations that deviate substantially from the overall pattern of the data.

Steps in statistical analysis

Nominal/categorical variables are, as the name suggests, variables which can be slotted into different categories (e.g., gender or type of psoriasis). A simple test which can be used to differentiate between qualitative and quantitative variables is the subtraction test. If you can subtract the value of one variable from the other to get a meaningful result, then you are dealing with a quantitative variable (this of course will not apply to rating scales/ranks). To reach from one place to another, we estimate the time it will take us to reach. We estimate the speed of the vehicle approaching while driving or crossing a road. Using these estimations, we tune in the time or other adjustments that need to be made.

Dispersion or variability describes the spread or variation present within a data. Descriptive Statistics is a sub-division of Applied statistics that quantifies the data. It provides a summary of the important characteristics or features of the data.