In statistics, there are two methods used to analyze data, namely descriptive statistics and inferential statistics. Inferential statistics is a method of analyzing data on a population based on sample data.
In this method the final result of the analysis is a possibility that describes how the sample’s actions can affect the entire population.
What is Descriptive Statistics?
Descriptive statistics describe, show, and summarize the basic features of a data set found in a particular study to make it easier to understand.
Then, these features are presented in a summary that describes the data sample and measurements. This will help analysts understand the data better.
Descriptive statistics represent a sample of available data and do not include theory, inference, probability, or conclusions. Because it is included in the realm of inferential statistics.
In addition, descriptive statistics describe data but do not attempt to draw conclusions from a sample of the entire population like inferential statistics.
Descriptive Statistics Methods
Using descriptive statistics, we can get various information from the data and variables.
1. Data centering on single data and group data (mean, median, and mode)
2. Single data layout size and grouped data (quartiles and deciles)
3. Measures of the spread of data on a single data set (range, inter-quartile range, deviation, mean deviation)
4. Estimates of data spread in grouped data (range, interquartile range, quartile deviation, standard deviation, and variance)
Mean, Median, and Mode
If you have been wondering about descriptive statistical formulas, you must first understand the mean, median, and mode.
1. The mean or average compares the total amount of data and the amount of data.
2. The median is the middle value of the existing data. I have my own way of memorizing what the median is. From what he wrote, initially, there was ‘med,’ like a medium. The medium is usually in the middle, below the large but above the small. So, I always remember that the median means the middle value.
3. The mode is the value that occurs the most frequently. In other words, the way is the value that occurs most often compared to different values.
Types of Descriptive Stastistics
Descriptive statistics are grouped into several types. According to its characteristics, descriptive statistics are divided into three types, namely:
1. Distribution
In this type, the dataset consists of a distribution of scores or grades. Statisticians use graphs and tables to summarize the frequency of each possible value of a variable. Then, the findings are displayed in the form of a percentage or number.
Let’s say you want to show data about your favorite football team competing in the 2022-2023 English Premier League series. Then, you need to prepare one column with all the variables (the 20 teams competing in the Premium League) and another column that lists the number of votes.
2. Central tendency
Central tendency estimates a data set’s mean or center point, finding the result using mean, mode, and median.
Mean (M) is the most common method of finding the average. You get the average by adding all the response values and dividing by the number of responses (N).
For example, you want to know the average height of students in class 7A. Thus, the height information for all class 7A was collected and summed up.
After that, it is divided by the number of students in class 7A.
The mode indicates the response value that occurs most often. To make it easier to find the way, you can order the dataset from lowest to highest, then look for the most common response.
For example, in measuring the height of class 7A students, the response value of 150 cm appears the most. So, 150 cm is the mode of measurement.
The median is the value that is the midpoint of the data set. To find out, arrange the data set from lowest to highest. For example, after a data set is sorted, the order appears like this: 2, 3, 4, 4, 5, 6, 8.
There are seven response values. That means the median is 4 because it is the midpoint of the data set.
3. Variability
Variability gives the statistician an idea of how spread out the response values are. There are three aspects to variability: range, standard deviation, and variance.
The content is used to determine how far the most extreme values are. The standard deviation is the average amount of dataset variability, while the conflict reflects the spread of the dataset degrees.
Descriptive Statistics Purpose
The primary purpose of descriptive statistical analysis is to provide an overview of the variables used, such as the minimum value, maximum value, average, and standard deviation in each study.
The descriptive statistical analysis summarizes the conditions and characteristics of the respondents’ answers for each construct or variable studied.
Descriptive analysis was carried out by presenting the data into a frequency distribution table, calculating the average value, total score, and the respondent’s achievement level (TCR), and interpreting it.
Descriptive statistical analysis aims to collect, process, and analyze data so that it can be presented in a better view.
Descriptive Analysis
Descriptive analysis is statistics that are used to analyze data by describing or describing the data that has been collected as it is without intending to make general conclusions or generalizations.
The descriptive analysis uses tools such as Microsoft Excel 2013, SPSS, or R. The results are in the form of diagrams such as pie charts, bar charts, and data analysis calculation tables.
The resulting output is in the form of tables and graphs so that the reader can immediately find out the information that has been processed from the data.
To carry out a descriptive analysis, we must first know the type or type of data.
After that, we can do a descriptive analysis covering several things: frequency distribution, central tendency measurement, and variability.
Descriptive research can include mean, median, standard deviation, range, and variance. The results of a descriptive analysis can also be presented in graphical form.
Descriptive Statistics vs Inferential Statistics: What’s The Differences?
The difference between descriptive and inferential statistics lies in the purpose, the process of analysis, and the presentation of the analysis results.
The first difference is the purpose of the statistical analysis. Descriptive statistics aim to explain known data characters. On the other hand, statistical inference aims at drawing conclusions about a population through sample analysis.
The next difference between descriptive and inferential statistics is in the data processing. The descriptive analysis uses relatively simple methods, such as averages and variances.
In contrast, inference analysis uses more complex techniques, such as comparing data and making predictions, so not everyone can use them.
The final difference between the two is how the final result is presented.
Descriptive Statistics show the results of their analysis as tables and graphs because it is helpful to describe certain situations.
In contrast, Inference Statistics presents the final results as probabilities because it aims to explain the possibility.