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Are you wondering, “what is the difference is between descriptive and inferential statistics?” You are not alone. The two types of statistics cause a lot of confusion.
Inferential and descriptive statistics are popularly used in a research study to simplify data. Nonetheless, whether you call it descriptive or inferential statistics, both have one end goal: analyzing data with statistics.
This article will help you understand the difference between descriptive and inferential statistics. You will also get definitions, examples, and real-life applications.
What is Descriptive Statistics?
Descriptive statistics describes the composition of samples. For example, the calculation of the following sample’s population average age (50, 40, 30) is 40 years ols. However, when you start including hypothesis in your research, you will in this case be shifting to inferential statistics.
The mean represents a descriptive statistic because it describes the data set or sample. The mean also denotes a measure of central tendency that describes where the center of data tends to offer incorrect information. Mean does not provide information about variability.
Standard deviation is an example of inferential statistic because it allows us to infer or guess data on population based on your data set. The standard deviation indicates the scope of values in a data.
Also, standard deviation determines whether your sample (in this case, 40 years old) represents the entire population well enough -- meaning, does your sample look like it is from the same parent population? If so, you can infer that your parent population has a mean of 40 years old.
What is Inferential Statistics?
On the other hand, inferential statistics is the process of concluding a population based on available information about a sample. This is different from descriptive statistics, which only provides information.
Inferential statistics draws generalizations from specific studies or experiments. An example of inferential statistics is a laboratory study on the effect of a drug on lab mice that might not apply to humans. A researcher would use inferential statistics to support or counter this hypothesis.
Descriptive statistics give you one number for all the data in your sample. In contrast, inferential statistics types can tell you any important differences between two groups. Also, if you have multiple independent samples and any trends within your sample, you have only one independent sample.
Difference between Descriptive and Inferential Statistics?
Descriptive statistics allows researchers to describe and explain information about people, places, or things. Researchers can break down the process into three parts: collecting raw data, organizing data in a meaningful manner, and creating graphs, charts, and tables to display data and analyze results.
Moreover, inferential statistics is the process of determining an appropriate hypothesis. The hypothesis can determine the proper action to take regarding the population. When it comes to inferential statistics, there are two types:
- Hypothesis testing and estimation
- Correlation analysis
Descriptive and Inferential Statistics Examples
Hypothesis testing uses information collected from a sample population to come up with conclusions about the larger population. In this case, you use some of the observations you get from your sample and extrapolate information about all of your observations in the entire population.
Inferential statistics are advanced statistical techniques that allow you to make accurate generalizations about a population based on information collected from a sample. The most common type of inferential statistical technique is probably hypothesis testing.
Statistical methods are also used to describe the characteristics of populations. Descriptive statistics summarize data from a population rather than using statistical methods to infer what a population might be like based on sample information.
What do descriptive statistics tell you about a data set?
Descriptive statistics refers to any numerical information you can collect from a data set. These numbers describe the data, such as the mean, median, and range. Descriptive statistics give an overview of a data set and may include descriptive statistics and visual graphics. A graph or chart that displays the numerical information about a data set is a descriptive statistic.
Descriptive statistics are powerful tools for summarizing your data and revealing key insights. For example, if you want to compare two populations, you might use descriptive statistics to find the average age in each population.
Afterward, you can determine whether there is a significant difference between the average ages of the two groups. Descriptive statistics are also useful for identifying outliers (atypical data points) in your sample.
What do inferential statistics tell you about a data set?
Inferential statistics tells you whether the data set was sampled randomly. When carrying out a survey, you are trying to figure out what people think or believe.
We call this "a population." The word "population" comes from the Latin word for city, because it is like surveying a city: The people whose opinions matter are all in the city.
The people whose opinions don't matter are outside the city. In statistics, we call these people "outliers," because they are outliers from the population we are interested in.
So how do you know whether the sample of answers you got reflects opinions in the city? It would help if you had inferential statistics, which are math formulas that tell you whether your sample was random and accurately reflects the population.
The descriptive vs inferential statistics debate continues. Whether you are an expert researcher or just a novice, there can be factors about these terms that may not be entirely clear.
Hopefully, this brief guide has equipped you with the knowledge to help you better understand statistics and how they differ.
As long as you take the time to consider research based on facts and data instead of emotions or feelings, you should be able to determine if it is of sufficient value in helping you make decisions.
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