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.

The main purpose of descriptive statistics is to provide a brief summary of the samples and the measures done on a particular study. Coupled with a number of graphics analysis, descriptive statistics form a major component of almost all quantitative data analysis.

Descriptive statistics are quite different from inferential statistics. Basically, descriptive statistics is about describing what the data you have shown.

For inferential statistics, you are trying to come up with a conclusion drawing from the data you have. For example, we can use inferential statistics to try and give an indication of what the population thinks from the sample. We can also use inferential statistics to judge on the probability of something occurring based on the behavior of the sample of data taken for a study.

Thus, inferential statistics are used mainly to infer based on the sample data we have at hand to make conclusions. On the other hand, descriptive statistics is used mainly to give a description of the behavior of the sample data.

Descriptive statistics are usually used in presenting a quantitative analysis of data in a simple way. In a study, there are quite a number of variables that are usually measured. Therefore, descriptive statistics comes in to break this numerous amounts of data into a simple form.

For example, one might be interested to find the average passes a footballer makes in a single match. Clearly, there are quite a number of activities in a single game; therefore we can use descriptive statistics to make this simpler.

Here we can get a single number that will help us describe very many discrete events.

Another instance is determining the how a student performs in school. Usually, we use the Grade Point Average.

This is just a single number that gives a general indication of the performance of a single individual. It is important to note that, when using a single value to describe a large set of data, there is a possibility that you are going to change the original meaning of the data or lose some important detail.

This is because; the number just gives an overall impression of the aspects but does not provide the exact detail of the same. For example, the GPA of a student does tell whether the student performed well in the easy subjects and failed the hard one or vice versa.

However, regardless of these shortcomings, descriptive statistics are still the best way of summarizing a wide range of data and aid in making comparisons between the same. This is basically the examination of different cases of a single variable at the same time.

There are three main areas that we are going to look at: The distribution The measure of central tendency The dispersion These are the common characteristics that we will want to identify in our variables.

The Distribution The distribution is a summary showing the frequency of single values of the ranges of a variable. A simple distribution table will list all their values against the number of persons or units each of them had.

For example, the simplest way of describing the distribution of university student based on their year of study is to list the percentage of students or the number of students in every year. We can also describe the gender of a sample by listing the percentage of males and females or the numbers of each.

In such cases, the variables involved are quite a few such that we are in a position to comfortably list all them and make a quick summary of the numbers involved in each value.

But then, there are cases where the number is too large, for example when handling the GPA or income. In such cases, there are quite a number of possibilities in terms of values and all the values will carry a few people.

In such cases, the raw scores need to be grouped in terms of a range of values. For example, the raw scores can be grouped in terms of the ranges of a letter grade e.Descriptive statistics are statistics that describe the central tendency of the data, such as mean, median and mode averages.

Variance in data, also known as a dispersion of the set of values, is another example of a descriptive statistics. Greater variance occurs when scores are more spread out. An approach to Descriptive Statistics through real situations Paula Lagares Barreiro1 Federico Perea Rojas-Marcos1 • To determine the parameters of an statistics distribution.

• To study the coeﬃcient of variation. • To motivate through information given in examples and exercises about social, ecological, economical topics, etc.

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.

May 06, · The Udemy course Descriptive Statistics in SPSS is a great tool to help you with descriptive statistics for incredibly large amounts. Exploring the Two Types of Descriptive Statistics The first type of descriptive statistics that we will discuss is the measure of central pfmlures.com: April Klazema. Descriptive statistics implies a simple quantitative summary of a data set that has been collected. It helps us understand the experiment or data set in detail and tells us everything we need to . Descriptive statistics is a branch of statistics that aims at describing a number of features of data usually involved in a study. The main purpose of descriptive statistics is to provide a brief summary of the samples and the measures done on a particular study.

Together with simple graphics analysis, they form the basis of virtually every quantitative analysis of data. Descriptive statistics is a branch of statistics that aims at describing a number of features of data usually involved in a study. The main purpose of descriptive statistics is to provide a brief summary of the samples and the measures done on a particular study.

Descriptive Statistics: What Are They? Which Ones Will The Love Canal Study Use? When we talk about using descriptive statistics, we mean that we plan to use statistical tools that describe the data in a way that we can better understand them (what patterns do the data show, if any?

how are they distributed? do they cluster in some way?). May 06, · The Udemy course Descriptive Statistics in SPSS is a great tool to help you with descriptive statistics for incredibly large amounts.

- Inferential Statistics
- Examples of Descriptive Statistics

Exploring the Two Types of Descriptive Statistics The first type of descriptive statistics that we will discuss is the measure of central pfmlures.com: April Klazema.

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What Are Some Examples of Descriptive Statistics? | pfmlures.com