Difference Between Descriptive and Inferential Statistics

Summary: Learn the critical differences between descriptive and Inferential Statistics and their types and practical applications. Descriptive Statistics summarise data, while Inferential Statistics make predictions and conclusions. Mastering both enhances your data analysis skills.

Introduction

Data Analytics and Data Science are required career fields in the market today. One of the essential parts of the career is Statistics. In the Mathematics branch, statistics helps conduct market analysis using bulk numerical data. There are two different types of Statistical Analysis in Data Analytics. Accordingly, it includes Descriptive Statistics and Inferential Statistics. 

In this blog, we will explore the differences between descriptive vs Inferential Statistics and explain the different types of these techniques in detail. Let’s get started!

What is Descriptive Statistics?

Descriptive Statistics is the branch of statistics that deals meaningfully with data collection, analysis, and presentation. Accordingly, Descriptive Statistics summarise and describe the critical features of a dataset, such as its central tendency, variability, distribution, and shape.

There are two main types of Descriptive Statistics: frequency distribution, central tendency measures and variability. 

Significantly, measures of central tendency include the mean, median, and mode, which describe a dataset’s typical or principal value. Measures of variability include the range, variance, and standard deviation, which represent how to spread out or variable the values in a dataset. 

What is Inferential Status?

Inferential Statistics is the branch of statistics that makes predictions or conclusions about a population by emphasising a sample of data from the people. Effectively, it involves using statistical methods to analyse and interpret data to make predictions and test hypotheses. 

Moreover, Inferential Statistics relies on Probability Theory and Mathematical methods. Hence, these methods help quantify the uncertainty and variability in the data from which data samples were taken.

Some standard techniques used in Inferential Statistics include hypothesis testing, confidence intervals, and regression analysis. Significantly, the use of Inferential Statistics is widely popular in the fields of science, business economics, social sciences, and healthcare. 

Accordingly, it helps these industries make decisions and draw practical conclusions based on empirical data. Moreover, the tool helps make informed decisions and assess the reliability and validity of the findings. 

Read Blog: Probability Distribution in Data Science: Uses & Types.

Types of Descriptive Statistics

Understanding the types of Descriptive Statistics is crucial as they provide essential tools for summarising and interpreting data. These statistics help identify patterns, trends, and insights, enabling informed decision-making and effective communication of data-driven findings across various fields and industries. There are three types of Descriptive Statistics, which can be explained in the following:

Frequency Distribution

It is a method of organising and summarising data using a table or graph that displays the frequency of each value or range of values within a dataset. Consequently, it often shows how the response is given for different qualitative and quantitative data types. 

Additionally, bar charts, histograms, pie charts and line charts are the most popular ways of presenting frequency distribution. Accordingly, each entry within the graph or table shows how often each value or group of values appears in a Dataset.

To create a frequency distribution, the data is first sorted into categories or intervals called bins, representing the range of values for counting. The frequency from each bin is calculated, and the results are presented in a table.

Central Tendency

It is a type of Descriptive Statistics in which the summary of the statistics is involved. It uses a single value reflecting the data distribution point. The central tendency is to locate the data distribution through various issues. 

Furthermore, it helps show the dataset’s average or most common responses. Measures of central tendency include the mean, median and mode.

Variability or Dispersion

Variability is a type of Descriptive Statistics that measures the extent to which the values in a dataset vary or spread out from each other. It summarises the amount of dispersion or diversity in a set of data. 

Statistics commonly uses different types of measures of variability, including range, variance, standard deviation, interquartile range, and coefficient of variation.

Types of Inferential Statistics

Understanding the types of Inferential Statistics is crucial because they allow us to make predictions and generalisations about a population based on sample data. This knowledge is essential for analysing data accurately, making informed decisions, and conducting meaningful research across various fields. The three main types of Inferential Statistics are mentioned below:

Regression Analysis

Finding the relationship between a set of independent and dependent variables is possible. With the help of regression models, The statistical model helps predict the dependent variable’s value, emphasising the independent variable’s values. 

Effectively, Hypothesis testing is part of the model that helps determine whether the relationship observed from the sample data is valid as per the dataset.

Hypothesis Test

Hypothesis testing is a method in Inferential Statistics that is used to compare entire populations or analyse relationships between variables through samples. By applying statistical tests, we can make valid inferences or predictions. 

This approach helps researchers determine if observed patterns or differences in the data are due to chance or if they signify an actual effect. Ultimately, hypothesis testing allows for informed decision-making based on statistical evidence, enhancing our understanding of the data and guiding further research.

Confidence Intervals

Confidence intervals are vital in Inferential Statistics. They help estimate population parameters when the actual values are unknown. By providing an interval estimate, confidence intervals allow researchers to observe the variability in a statistic. 

This method highlights the range within which the proper parameter will likely fall and quantifies the uncertainty associated with the estimate. Consequently, confidence intervals offer a clearer understanding of the reliability and precision of statistical inferences.

Difference between Descriptive and Inferential Statistics

The differences between descriptive and Inferential Statistics lie within the process of the statistical reports. Following are the key differences that you will need to know about descriptive and Inferential Statistics:

• Descriptive Statistics provides you with information on raw data based on the description or features. On the other hand, Inferential Statistics extract inferences on the sample population using data derived from the people.
• You can use Descriptive Statistics to describe a situation. In contrast, Inferential Statistics can be used to explain an event’s probability of occurrence.
• Significantly, Descriptive Statistics help organise, analyse, and present data in an understandable format. Inferential Statistics help compare data and make hypotheses and predictions.
• The data relevant to a particular sample or population is crucial in Descriptive Statistics and, hence, is easy to explain. However, Inferential Statistics concludes a group of people.
• Moreover, Descriptive Statistics represent the data using charts, graphs, and tables. On the other hand, probability methods are helpful in Inferential Statistics.
• Furthermore, a study is more straightforward to perform using Descriptive Statistics rather than Inferential Statistics because you can establish a relationship between the entire population.

Must See: 

Crucial Statistics Interview Questions for Data Science Success.

Best Statistics Books for Data Science.

Frequently Asked Questions

What is the main difference between descriptive and Inferential Statistics? 

Descriptive Statistics summarise and describe the main features of a dataset through measures like mean, median, and mode, as well as graphs and tables. Inferential Statistics, on the other hand, use sample data to make generalisations and predictions and draw conclusions about a larger population.

What are the types of Descriptive Statistics? 

Descriptive Statistics include frequency distribution, which organises data into a table or graph; measures of central tendency (mean, median, mode) that indicate the central point of a dataset; and measures of variability (range, variance, standard deviation) that describe the spread or dispersion of data points.

How are Inferential Statistics used in real-life scenarios? 

Inferential Statistics are applied in fields like healthcare to determine treatment effectiveness, in business for market analysis and forecasting, and in social sciences to study behavioural trends. They help make data-driven decisions, predict future outcomes, and validate hypotheses by analysing sample data and extending the findings to a broader population.

Conclusion

Hence, the above blog provides you with details on the differences between Descriptive Statistics and Inferential Statistics. You must ensure that you clearly understand your concepts and skills in this field while you embark on a Data Science journey. 

Statistics is crucial for a Data Science career. You can develop these skills if you join one of Pickl.AI’s Data Analytics courses, where you will learn all about statistics and the types of statistical methods effectively.