They provide simple summaries about the sample and the measures. Together with simple graphics analysis, they form the basis of virtually every quantitative analysis of data.
Typically, in most research conducted on groups of people, you will use both descriptive and inferential statistics to analyse your results and draw conclusions.
So what are descriptive and inferential statistics? And what are their differences? Descriptive Statistics Descriptive statistics is the term given to the analysis of data that helps describe, show or summarize data in a meaningful way such that, for example, patterns might emerge from the data.
Descriptive statistics do not, however, allow us to make conclusions beyond the data we have analysed or reach conclusions regarding any hypotheses we might have made. They are simply a way to describe our data. Descriptive statistics are very important because if we simply presented our raw data it would be hard to visulize what the data was showing, especially if there was a lot of it.
Descriptive statistics therefore enables us to present the data in a more meaningful way, which allows simpler interpretation of the data. For example, if we had the results of pieces of students' coursework, we may be interested in the overall performance of those students.
We would also be interested in the distribution or spread of the marks. Descriptive statistics allow us to do this.
How to properly describe data through statistics and graphs is an important topic and discussed in other Laerd Statistics guides. Typically, there are two general types of statistic that are used to describe data: Measures of central tendency: In this case, the frequency distribution is simply the distribution and pattern of marks scored by the students from the lowest to the highest.
We can describe this central position using a number of statistics, including the mode, median, and mean.
You can read about measures of central tendency here. For example, the mean score of our students may be 65 out of However, not all students will have scored 65 marks. Rather, their scores will be spread out.
Some will be lower and others higher. Measures of spread help us to summarize how spread out these scores are. To describe this spread, a number of statistics are available to us, including the range, quartiles, absolute deviation, variance and standard deviation.
When we use descriptive statistics it is useful to summarize our group of data using a combination of tabulated description i.To understand the simple difference between descriptive and inferential statistics, all you need to remember is that descriptive statistics summarize your current dataset and inferential statistics aim to draw conclusions about an .
To understand the simple difference between descriptive and inferential statistics, all you need to remember is that descriptive statistics summarize your current dataset and inferential statistics aim to draw conclusions about an additional population outside of your dataset.
Descriptive statistics are used to describe the basic features of the data in a study. They provide simple summaries about the sample and the measures. Together with simple graphics analysis, they form the basis of virtually every quantitative analysis of data. Reporting Results of Descriptive and Inferential Statistics in APA Format The Results section of an empirical manuscript (APA or non-APA format) are used to report the quantitative results of descriptive statistics and inferential statistics that were applied to a set of data.
Descriptive and inferential statistics are two broad categories in the field of benjaminpohle.com this blog post, I show you how both types of statistics are important for different purposes.
Interestingly, some of the statistical measures are similar, but the goals and methodologies are very different. Descriptive and inferential statistics each give different insights into the nature of the data gathered. One alone cannot give the whole picture.