1148 LearnersLast updated on June 5th, 2025
Descriptive and Inferential Statistics
Collecting, organizing, analyzing, and interpreting data are some of the few features of statistics. Statistics is generally divided into two branches: descriptive statistics and inferential statistics.
Descriptive and Inferential Statistics
Descriptive statistics is about presenting data in a way that is easy to understand. It includes measures such as mean, median, mode, standard deviation, and graphs. It is about creating a clear visual representation of the dataset. In descriptive statistics, we do not make predictions or generalizations, but rather describe what is observed.
Inferential Statistics involves techniques like hypothesis testing, confidence intervals, and regression analysis. This allows us to conclude trends and test relationships. Inferential statistics helps us draw conclusions and make predictions for a large population based on a sample.
Both descriptive and inferential statistics are the foundation of data analysis and help researchers and businesses in making decisions based on data.
Struggling with Math?
Get 1:1 Coaching to Boost Grades Fast !
Difference between Inferential and Descriptive Statistics
Descriptive and inferential statistics are fundamental branches of statistics. Each serves its purpose in data analysis. Here are the differences between inferential and descriptive statistics:
|
Descriptive Statistics |
Inferential Statistics |
| Summarizes, describes, and presents the main features of a dataset. | Makes predictions, concludes, or generalizations about a population based on a sample. |
|
Focuses on the entire dataset or the sample only. |
Focus on a sample of data to make inferences about a larger population. |
|
Helps understand what the data is about. |
Helps predict or make conclusions about a larger dataset. |
| Some of the methods are mean, median, mode, standard deviation, and graphs (bar graphs, pie charts, etc.). | A few features we use are hypothesis testing, probability, and confidence intervals. |
| Calculating the average height and creating a histogram for the heights of all students in a class. |
Conducting a hypothesis test to determine if the average height of students is different from the national average height for students of the same age. |
| Exact numbers based on the collected data. | It is an estimate or prediction of data with some uncertainty. |
Similarities between inferential and descriptive statistics
Here are some common similarities between the two branches of statistics:
- Data analysis: Both branches involve analyzing data to extract information.
- Statistical Techniques: Both descriptive and inferential use statistical methods and tools to analyze data.
- Complementary: Descriptive statistics is often the first step in data analysis, providing a summary of the data. Inferential statistics builds on this data to make conclusions or predictions about the population.
- Applications: Descriptive statistics and inferential statistics both are widely applied in various fields including science, businesses, social sciences, and healthcare. They play a vital role in decision-making, research analysis, and problem-solving.
What are the types of Descriptive and Inferential statistics?
Descriptive and inferential statistics both use statistical tools that are specific for each branch:
Some types of descriptive statistics are:
- Measures of Central Tendency:
- Mean
- Median
- Mode
- Measure of Dispersion:
- Range
- Variance
- Standard Deviation
- Interquartile Range
- Graphical Representations
- Histograms
- Bar charts
- Pie charts
- Scatter plots
Some types of inferential statistics are:
- Hypothesis Testing
- t-Tests
- Chi-Square Tests
- Z-Tests
- Confidence Intervals
- Mean Confidence Interval
- Proportion Confidence Interval
- Regression Analysis
- Simple linear regression
- Multiple linear regression
What are the tools used for descriptive and inferential statistics?
Some of the tools that we can use to calculate any of the two branches of statistics are as follows:
- For Descriptive Statistics:
- Microsoft Excel: Excel is a very common tool used to calculate central tendency and dispersion measures. It is also used to create graphical representations such as histograms and scatter plots.
- Statistical Package for the Social Sciences (SPSS): It is a statistical software package used for data management, analysis, and reporting. Frequency distributions and descriptive charts are some of the features that SPSS offers.
- R: R is a programming language and software environment that is specially designed for statistical computing and graphics.
- Python: This is another programming language with libraries such as NumPy, Pandas, and Matplotlib, and is very popular for statistical analysis and data visualizations.
- For Inferential Statistics:
- R: R programming is also useful for inferential statistics as well. It offers various packages for conducting hypothesis testing, regression analysis, and confidence interval estimations.
- SPSS: Other than descriptive statistics, SPSS provides tools for conducting tests like t-tests, chi-squared tests, etc.
- Python: StatsModels and scikit-learn are some of the libraries that help in conducting various inferential statistical analyses.
- SAS (Statistical Analysis System): A statistical software that is used for data management and reporting.
Real-life applications on Descriptive and inferential statistics
Statistics is widely used by researchers and businesses to analyze data. Here are a few real-world applications of descriptive and inferential statistics:
Healthcare
Descriptive Statistics: To track mortality rates or patients' ages, hospitals use descriptive statistics to understand health trends.
Inferential Statistics: Clinics use sample data to predict how a drug performs generally.
Sports
Descriptive Statistics: Teams track player performance by calculating their average goals per match or shooting accuracy by using mean or other measures of central tendency.
Finance
Descriptive Statistics: Governments use descriptive statistics to summarize GDP growth or population growth.
Inferential Statistics: To predict future economic growth, economists use sample data and analyze future economic conditions.
Common mistakes and how to Avoid Them in Descriptive and inferential statistics
When learning descriptive and inferential statistics, students might make a few mistakes. Here are a few common mistakes that students make and ways to avoid them:
Mistake 1
Getting mean, median, and mode confused with each other
Students should always check which measure of central tendency is appropriate. Using the mean, when the median is more appropriate, will be incorrect.
Mistake 2
Misinterpreting the Standard Deviation values
Students might mistakenly assume that a higher standard deviation value results in an error. It must be known that standard deviation measures spread and not correctness.
Mistake 3
Not choosing the appropriate graphs for the dataset
When creating a graph for a particular dataset, students should ensure that the graph they select is appropriate for the dataset.
For example, we use bar charts to display data containing various categories.
Mistake 4
Misinterpreting p-values
Remember that p-values represent the probability of obtaining the observed result if the null hypothesis is true.
Mistake 5
Overlooking the sample size requirements
Many students use a small sample and try to make conclusions about a population. This increases the risk of getting a wrong conclusion. To avoid this, students can use a general sample size to get a more accurate estimate.
Level Up with a Math Certification!
2X Faster Learning (Grades 1-12)
Solved examples on Descriptive and inferential statistics
Problem 1
Calculate the mean of the following data set: 10, 15, 20, 25, 30.
20
Explanation
Sum of the data values (10 + 15 + 20 + 25 + 30 = 100) and divide by the number of total values, which is 5.
Mean = 100/5
= 20.
Problem 2
Find the median of the following dataset: 7, 3, 9, 5, 1.
5
Explanation
Arrange the data in ascending order: 1, 3, 5, 7, 9.
The median is the middle value for odd numbers:
So here it is 5.
Problem 3
Determine the mode of the dataset where the given data is: 4, 8, 2, 5, 6, 4, 9
4
Explanation
The mode is the value that appears most frequently.
The number 4 appears three times here.
So the mode of the dataset is 4.
Problem 4
Conduct a t-test to determine if there is a significant difference in the mean scores of two groups: Group A (scores are: 80, 85, 90, 95, 100) and Group B (scores: 75, 80, 85, 90, 95)
t-value = 2.04.
Explanation
Calculate the means and standard deviations of both groups. Use the t-test formula to find the t-value and compare it to the critical value t-value for the given degrees of freedom and significance level.
Problem 5
Calculate the range of the following dataset: 12, 18, 15, 22, 10.
12
Explanation
The range is the difference between the maximum and minimum values.
Maximum = 22,
Minimum = 10.
Range = 22 – 10
= 12.
Turn your child into a math star!
#1 Math Hack Schools Won't Teach!
FAQs on Descriptive and inferential statistics
1.What is descriptive statistics?
2.What are measures of central tendency?
3.What is Inferential Statistics?
4.What is meant by hypothesis testing?
5.What are some of the common tools used in descriptive and inferential statistics?
6.How can children in Thailand use numbers in everyday life to understand Descriptive and Inferential Statistics?
7.What are some fun ways kids in Thailand can practice Descriptive and Inferential Statistics with numbers?
8.What role do numbers and Descriptive and Inferential Statistics play in helping children in Thailand develop problem-solving skills?
9.How can families in Thailand create number-rich environments to improve Descriptive and Inferential Statistics skills?
Struggling with Math?
Get 1:1 Coaching to Boost Grades Fast !
Jaipreet Kour Wazir
About the Author
Jaipreet Kour Wazir is a data wizard with over 5 years of expertise in simplifying complex data concepts. From crunching numbers to crafting insightful visualizations, she turns raw data into compelling stories. Her journey from analytics to education ref
Fun Fact
: She compares datasets to puzzle games—the more you play with them, the clearer the picture becomes!
