The form of data analysis that is always useful in research is descriptive statistics vs inferential statistics. Both of them have different qualities, but they complement each other.

As a researcher, you must know when to use descriptive statistics and when to employ inference statistics. Your study results will be much more beneficial if you use both of them properly.

In this blog, we will try to explain and provide necessary details about how the two forms of analysis should perform in order to avoid colliding. So, let’s get into the details.

What are descriptive statistics?

Descriptive statistics is a quantitative concept for summarizing, describing, and displaying data. When the word “statistics” gets spoken, this is typically the sort of data analysis that jumps into mind.

Let’s imagine we’re researching an ice cream business that sells a variety of flavors. If they sold 100 cones and 60 of them were chocolate. Then one way to describe the ice cream sold information is that the chocolate flavor accounted for 60% of the overall sales.

We may also establish an average each month by looking at how many strawberry ice cream they sold each day during the month. This is a type of descriptive statistics as well. We can’t, for example, estimate how many strawberry cones are sold throughout the world using this sort of data.

Descriptive statistics do not allow us to make inferences based on data we currently know or to reach conclusions about hypotheses we may have. It’s just a method for organizing and describing facts in a usable way.

What are inferential statistics?

By evaluating a statistical sample, inferential statistics reduce the attributes of a population. Let’s use the ice cream store as an example. Let’s interpret this. Suppose we want to know how many ice creams are sold in a week throughout the entire state of New York.

It’s practically impossible to visit every single business, restaurant, and parlor in the neighborhood, which is where inferential statistics come into play.

To find out, you’d simply take a representative sample, such as a set of businesses and parlors scattered over New York. And collect the results from the entire population.

While the strategy does not guarantee 100 % accuracy, as you might expect, it does allow us to make well-founded assumptions about the group we’re researching.

Descriptive statistics vs inferential statistics: Detailed differences to learn

Let’s check the basic distinctions between these two forms of analysis.

1. The goal distinction

The purposes of descriptive statistics and inferential statistics are very different. The purpose of descriptive statistics is to make data more meaningful and understandable.

On the other hand, inferential statistics are concerned with making a prediction, concluding, or testing a hypothesis about a population based on a sample.

There are two types of analyzes that may be used to fulfil the goal of descriptive statistics:

1. Quantitative analysis

2. Visualization of data

These two methods of descriptive analysis can help you extract valuable information from your data.

Remember that descriptive statistics cannot utilise to draw conclusions from your data. It is just to describe the facts and qualities.

On the other hand, the inferential analysis might use to provide an analysis of the population that the samples represent.

There are four forms of inferential analysis that researchers typically employ:

1. Making deductions

2. Testing hypotheses

3. Establishing connections

4. Making forecasts

If you wish to draw a conclusion based on your data, you can apply any of these four analyzes.

2. Differentiation in Complexity

In reality, descriptive statistics are more user-friendly than inferential statistics.

Descriptive statistics, as you may know, only employ fundamental formulas. And these are mean, mode, median, variance, standard deviation, and so on. It’s simple to use since all you have to do is enter a value into the formula and wait for the results.

Otherwise, inferential statistics allow you to go a step further in your study. It is by allowing you to conduct an analysis that might lead to a conclusion.

As previously said, you may use correlation and regression to determine relationships between variables, hypothesis testing, or a statistical model to generate predictions.

It’s complicated, and inferring data requires a lot of processes.

3. The variable count differs

Based on the number of variables, there are three methods of data analysis:

1. A single-variable analysis

The study of the distribution of only one variable is known as univariate analysis.

2. Bivariate statistical analysis

The evaluation of two variables at the same time consider as bivariate analysis.

3. Multivariate statistical analysis

The investigation of more than two variables at the same time is known as multivariate analysis.

The only univariate analysis uses descriptive statistics. That is to say, it can only explain the qualities of one variable. It can’t utilise to figure out if there’s a link between two or more variables.

Inferential analysis might be applied to all three variables in the opposite direction. You may do hypothesis testing on one or two variables, identify the link between variables, and so on.

Let’s wrap it up!

The following are the differences between descriptive statistics vs inferential statistics:

1. Goals are different.

2. The level of difficulty.

3. The number of variables differs.

Inferential statistics are useful to draw conclusions about the population. And these depend on the sample we have. Descriptive statistics are also useful to describe the general conditions and characteristics of the data. Whereas inferential statistics are useful to draw conclusions for the population based on the sample we have.

Do you have a different viewpoint on descriptive statistics vs inferential statistics? Please leave the respective comment below!