# Provide an example of the relationship between descriptive and inferential statistics

### What the relationship between descriptive and inferential statistics? | Socratic

Descriptive statistics provides us the tools to define our data in a The goal of the inferential statistics is to draw conclusions from a sample and. The primary difference between descriptive and inferential statistics is that Function, It explains the data, which is already known, to summarize sample. While descriptive statistics provide the summation of the data the. For example the sample mean and standard deviation provide estimates of the equivalent population parameters. Even descriptive statistics.

## Difference Between Descriptive and Inferential Statistics

Our data is the information collected from the population. Population is always defined first, before starting the data collection process for any statistical study. Population is not necessarily be people rather it could be batch of batteries, measurements of rainfall in an area or a group of people. It is the part of population which is selected randomly for the study.

### Difference Between Descriptive and Inferential Statistics (with Comparison Chart) - Key Differences

The sample should be selected such that it represents all the characteristics of the population. The process of selecting the subset from the population is called sampling and the subset selected is called the sample. Data can be summarized and represented in an accurate way using charts, tables and graphs.

We have marks of students and we may be interested in the overall performance of those students and the distribution as well as the spread of marks. Descriptive statistics provides us the tools to define our data in a most understandable and appropriate way. Inferential Statistics It is about using data from sample and then making inferences about the larger population from which the sample is drawn.

The goal of the inferential statistics is to draw conclusions from a sample and generalize them to the population. It determines the probability of the characteristics of the sample using probability theory. The most common methodologies used are hypothesis tests, Analysis of variance etc. 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.

### Understanding Descriptive and Inferential Statistics

Join the 10,s of students, academics and professionals who rely on Laerd Statistics. For example, we could calculate the mean and standard deviation of the exam marks for the students and this could provide valuable information about this group of students.

Any group of data like this, which includes all the data you are interested in, is called a population.

A population can be small or large, as long as it includes all the data you are interested in. For example, if you were only interested in the exam marks of students, the students would represent your population. Descriptive statistics are applied to populations, and the properties of populations, like the mean or standard deviation, are called parameters as they represent the whole population i. Often, however, you do not have access to the whole population you are interested in investigating, but only a limited number of data instead.

For example, you might be interested in the exam marks of all students in the UK.