What Is Descriptive Statistics? Make Good Business Decision

Descriptive Statistics

Still not very clear about what descriptive statistics is and why it is important? Rest assured, because today we will tell you everything you need to know about this term to take advantage of statistical analysis in daily life and in business, aiming to make better decisions.

The use of this discipline is not only limited to processing and organizing large volumes of information or big data for organizational purposes, but you can also take advantage of statistics for your personal finances or other interests since it allows you to draw conclusions about all kinds of data.

Statistics studies the behavior of data. Thanks to this exact science we can build our conclusions on these data based on the different variables that have been applied”.

So, if you want to know more about this branch of statistics, continue reading the note and learn more about its definition and types. 

Descriptive Statistics: Definition

Do you want to know what descriptive statistics are? Basically, it is a branch of statistics that, as its name suggests, is responsible for quantitatively describing information. That is, its function consists of collecting, structuring, tabulating, organizing, and processing the data to infer the behaviors or characteristics of a certain group. 

Descriptive statistics are used with the aim of understanding certain phenomena and predicting events, based on the information it stores, summarizes, and organizes in tables, graphs, or other resources. 

In this way, this science is presented both in situations of daily life and in the various branches of study that exist, in order that better decisions can be made, through the study and interpretation of the information summarized in graphics. 

For example, imagine you are about to launch a sportswear business. In that case, the first thing you should do is a market analysis that allows you to obtain data related to the behavior of the public you plan to target.

Why should you carry out a market study? Well, it will give you the possibility to know which is the best channel to communicate with your audience, and where to advertise your brand, among other variables. 

In the example mentioned, descriptive statistics have been present during the research process of the individual, in which he has had to review statistics and numerical data to know the characteristics of consumers, with the aim of proposing more effective strategies and making better decisions. 

“Information helps us build good decisions, for this we need practical tools that speed up data analysis.”

Descriptive Statistics: Graphs and tables

Now that you are clear about what descriptive statistics are, you need to know what kind of tables, graphs, or other resources this discipline uses to organize and summarize information, so that you can analyze the data in a more practical way.

Certainly, there is a great variety of options that can be used by this branch of statistics when structuring the information; however, this time we have compiled some types of graphs that are frequently used so that you know them.

  • bar graph
  • column chart
  • Pie chart
  • line chart
  • pie charts
  • trend graph
  • Scatter plot
  • probability tables
  • Two-dimensional tables
  • histogram etc.

Types of Descriptive Statistics

Perfect, we’ve come a long way! You already know what descriptive statistics are and what are the most common tools that it usually uses to summarize data. The next thing we will do is point out what the types are and what each one consists of.

Next, we will mention what are the types of descriptive statistics:

Central Tendency

The central tendency is a type of descriptive statistics that is related to the intention of making a descriptive summary of a data set in a single value. Measures of central tendency include the following:

  • Mean: This measure of central tendency refers to the average of a data set that is obtained by adding all the values ​​that have been collected between the number of values ​​of the same set. 

    For example, you have 5 black pants, each from a different brand. The prices of each are $40, $50, $60, $60, $58. In this case, to get the average you must add “40 + 50 + 60 + 60 + 58”. Which will give you a value of $268. 

    The next thing you will have to do is divide that amount by the number of pants. That is, 268/5, in this way, you will get the average price of the pants, which is 53.6$. which represents the mean. 
  • Median: Another measure used for this type of descriptive statistics is the median, which can be calculated by ordering the values ​​in ascending order. The only thing you have to do to identify this measure of central tendency is to locate yourself at the center value. 

    Following the example that we proposed to explain the mean, the median would be $58, because after ordering the values ​​in the following way: “$40, $50, $58, $60, $60”, you will be able to identify that this is the number in the center. 
  • Mode: Finally, the mode refers to the value that is repeated the most within a data set. Based on the example that we have been seeing, the mode would be $60, since it is the value that is repeated the most in the sequence presented.

Frequency Distribution 

The measures of shape or distribution allow us to know how the values ​​that have been represented by tables or graphs of descriptive statistics are close or separated. That is, they are intended to show how the values ​​are gathered, with respect to the frequency with which they are related to the data set. 

In order to know how frequencies are distributed and expressed, it is necessary to know the following measurements:

  • Absolute frequency: Absolute frequency refers to the number of times a value appears in the set. 
  • Accumulated absolute frequency: The relative absolute frequency of a value refers to the sum of the absolute frequencies equal to or less than the same value, which appears within the data set.
  • Relative Frequency: The relative frequency of a value refers to the absolute frequency divided by the amount in the sample. 
  • Cumulative relative frequency: To calculate the cumulative relative frequency, the cumulative absolute frequency must be divided by the amount of the sample.

Dispersion Or Variability Measurement

Finally, other types of descriptive statistics that exist are the measures of dispersion or variability, which are responsible for showing us how variable the data are and how far they are in a sample, with respect to the arithmetic means.

This type of statistics, then, allows knowing the range or distance that the values ​​of a series or sequence have. To do this, various measures of dispersion are used that allow knowing the intervals and representing various components of variability.

Next, we will explain what the various measures of dispersion are and what they consist of:

1. Range: The range is responsible for measuring the amplitude of the values ​​of a data set. To obtain this measure of dispersion, you will need to calculate the difference between the highest and lowest value

2. Variance: The variance measures the distance between the mean and the values ​​of the sequence. To do this, you must add the squared differences of each value and the mean and divide that value by the sample number. 

3.- Typical or standard deviation: This measure of dispersion is calculated by taking the square root of the variance.  

Super! We have already reached the end of the article, but we are sure that you have been able to clarify your doubt about what descriptive statistics is and how it contributes to business and to the understanding of certain phenomena. 

We are waiting for you in the next article!

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