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For example, we could calculate the mean and standard deviation of the exam marks for the 100 students and this could provide valuable information about this group of 100 students. We have seen that descriptive statistics provide information about our immediate group of data. When we use descriptive statistics it is useful to summarize our group of data using a combination of tabulated description (i.e., tables), graphical description (i.e., graphs and charts) and statistical commentary (i.e., a discussion of the results). To describe this spread, a number of statistics are available to us, including the range, quartiles, absolute deviation, variance and standard deviation. Measures of spread help us to summarize how spread out these scores are. However, not all students will have scored 65 marks. For example, the mean score of our 100 students may be 65 out of 100. Measures of spread: these are ways of summarizing a group of data by describing how spread out the scores are.You can learn more in our guide: Measures of Central Tendency. We can describe this central position using a number of statistics, including the mode, median, and mean. In this case, the frequency distribution is simply the distribution and pattern of marks scored by the 100 students from the lowest to the highest. Measures of central tendency: these are ways of describing the central position of a frequency distribution for a group of data.
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Typically, there are two general types of statistic that are used to describe data:
#Stata descriptive statistics how to
How to properly describe data through statistics and graphs is an important topic and discussed in other Laerd Statistics guides. Descriptive statistics allow us to do this. We would also be interested in the distribution or spread of the marks. For example, if we had the results of 100 pieces of students' coursework, we may be interested in the overall performance of those students. Descriptive statistics therefore enables us to present the data in a more meaningful way, which allows simpler interpretation of the data. They are simply a way to describe our data.ĭescriptive statistics are very important because if we simply presented our raw data it would be hard to visualize what the data was showing, especially if there was a lot of it. 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. So what are descriptive and inferential statistics? And what are their differences? Descriptive Statisticsĭescriptive 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. Typically, in most research conducted on groups of people, you will use both descriptive and inferential statistics to analyse your results and draw conclusions. When analysing data, such as the marks achieved by 100 students for a piece of coursework, it is possible to use both descriptive and inferential statistics in your analysis of their marks.