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Descriptive and Inferential Statistics

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Statistics can be broken into two basic types.

The first is known as descriptive statistics. This is a set of methods to describe data that we have collected.

Ex. Of 350 randomly selected people in the town of Luserna, Italy, 280 people had the last name Nicolussi. An example of descriptive statistics is the following statement :

"80% of these people have the last name Nicolussi."

Ex. On the last 3 Sundays, Henry D. Carsalesman sold 2, 1, and 0 new cars respectively. An example of descriptive statistics is the following statement :

"Henry averaged 1 new car sold for the last 3 Sundays."

These are both ...view middle of the document...

"Henry sold 0 cars last Sunday because he fell asleep in one of the cars on the lot."

Again, this statement is not verifiable based upon the information provided.

The major use of inferential statistics is to use information from a sample to infer something about a population.

Descriptive and Inferential Statistics

https://statistics.laerd.com/statistical-guides/descriptive-inferential-statistics.phphttps://statistics.laerd.com/statistical-guides/descriptive-inferential-statistics.php

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When analysing data, for example, 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. 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, as 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 allow 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 100 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: these are ways of describing the central position of a frequency distribution for a group of data. 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. 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.

• Measures of spread: these are ways of summarizing a group of data by describing how spread out the scores are. For example, the mean score of our 100 students may be 65 out of 100. However, not all students will have scored 65 marks. Rather, their scores will be spread out. Some will be lower and...

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