In research activities, researchers need to understand the use of descriptive statistics and inferential statistics. Researchers can use one of them or combine both descriptive and inferential statistics.

Due to the importance of understanding descriptive and inferential statistics, researchers should be able to distinguish between the two. Based on this background, Kanda Data will discuss the difference between descriptive and inferential statistics for research activities.

In various research publications, researchers generally focus on using inferential statistics. However, in some publications, descriptive statistics are also commonly used even though the results of descriptive statistics are only presented in the narrative.

Then, what is the difference between descriptive statistics and inferential statistics? Descriptive statistics focus on central tendency, data variability, and distribution of sample/population data.

Meanwhile, inferential statistics are statistics used to obtain conclusions from the characteristics of a population. In inferential statistics, we can make generalizations from population characteristics. For more details, we will discuss the differences and examples between the two types of statistics in more depth.

**Descriptive statistics**

Descriptive statistics are relatively simpler than inferential statistics. Based on what I have conveyed before, descriptive statistics are more focused on measures of central tendency. Examples of measures of central tendency are mean, median, mode, etc.

In addition, descriptive statistics also focus on measures of data variability, for example, variance and standard deviation. As for the size of the distribution of sample/population data, it can be obtained from the distribution of probability, skewness, kurtosis, etc.

**Inferential Statistics**

Inferential statistics are statistics that researchers most often use to make conclusions from the characteristics of a population. Inferential statistics are more complex than descriptive statistics.

Inferential statistics can be used to generalize a population. In a large population, researchers can determine a representative sample of the population. The sampling technique must fulfill statistical rules.

Many analytical methods use inferential statistics. One example is the use of regression analysis. Regression analysis is one of the analytical methods of inferential statistics to determine the effect of independent variables on the dependent variable.

Other analytical methods that use inferential statistics are correlation analysis, t-test, ANOVA test, etc. In inferential statistics, the researcher will develop a hypothesis, then statistically test the hypothesis.

Now is the time for us to recap the topic of discussion on this occasion. Descriptive statistics describe the characteristics of the sample/population, for example, mean, median, mode, variance, standard deviation, etc. Meanwhile, inferential statistics are used to obtain conclusions from the characteristics of a population, for example, regression, correlation, etc.

Well, that’s the topic of discussion on this occasion. Hopefully, this article can be helpful for all of you. See you in the following article update!

Kanda DataThank you.. Please enjoy