Difference Between Descriptive and Inferential Statistics

Learn about the difference between descriptive statistics and inferential statistics in this article. Descriptive statistics is used to summarize and describe data, while inferential statistics is used to make inferences and predictions about a population based on a sample of data. Discover the goals, methods, and scope of these two branches of statistics and how they complement each other in providing a comprehensive analysis of datasets.

Statistics is a branch of mathematics that deals with data collection, analysis, interpretation, presentation, and organization. It is used to make a meaningful decision based on the data available. Statistics is broadly categorized into Descriptive Statistics and Inferential Statistics.

Descriptive Statistics summarizes and describes the data set. And inferential statistics make conclusions about a large population based on the sample of data.

Must Check: Basics of Statistics for Data Science

This article will explore the key differences between Descriptive Statistics and Inferential Statistics.

So, without further delay, let’s start.

Table of Content

• Descriptive Statistics vs Inferential Statistics
• Descriptive Statistics
  • Types of Descriptive Statistics
• Inferential Statistics
  • Types of Inferential Statistics
• Key Differences and Similarities

What is the Difference Between Descriptive Statistics and Inferential Statistics?

What is Descriptive Statistics?

It involves summarizing and describing a set of data. It is used to describe the central tendency of a set of data using a single value (mean, median, and mode) by identifying the central position. Descriptive Statistics is also used to describe the variability of the data, such as range, variance, or standard deviation. This help to get an idea of how spread the data is.

Types of Descriptive Statistics

Descriptive Statistics is broadly classified into two categories:

Measure of Central Tendency

A measure of central tendency is a summary measure that attempts to describe a whole set of data with a single value that represents the middle or centre of its distribution.

• Mean: Mean is defined as the sum of observations divided by the number of observations.
• Median: Median is a variable value that divides it into two equal parts when the data is arranged in order ( ascending or descending).
• Mode: Mode is the observation that occurs most frequently in a dataset and around which the observation of the dataset is clustered densely.

Measure of Variability (Dispersion)

Statistical methods that help to know about the distribution or the spread of the data points in the datasets are known as Measures of Dispersion.

• Range: Range is defined as the difference between the highest value and the lowest value.
• Variance: Variance is defined as the average squared difference from the mean. It measures how far each data point in the dataset is from the mean.
• Standard Deviation: It is defined as the square root of the standard deviation.

What is Inferential Statistics?

Inferential statistics takes samples from the population and makes inferences about the population. In simple terms, Inferential statistics draw conclusions or make predictions about the population based on the observed data.

It involves hypothesis testing to test the hypothesis that is made from the sample data (or observed data). Once the hypothesis testing is done, the p-value is calculated. p-value calculates the probability of observing the results if the null hypothesis is true.

Types of Inferential Statistics

The most common methodologies in Inferential Statistics are Hypothesis Testing, Confidence Interval, and Regression Analysis

Regression Analysis

A statistical method to model the relationship between a dependent (target) variable and independent (one or more) variables. These models are used to predict continuous data.

Confidence Interval

It is the range of values you expect your estimate to fall between. It is defined as the mean of the estimate plus minus the variation in that estimate.

Hypothesis Testing

A type of statistical analysis that is used to test the assumption made for the population parameters. A hypothesis test can be two-tailed or one-tailed (left or right). It consists of

• z-test
• t-test
• f-test
• chi-square test

What are the Similarities and Differences Between Descriptive and Inferential Statistics?

• Both Descriptive and Inferential Statistics are used to analyze data and provide into a particular phenomenon or population.
• Both involve data visualization techniques, such as histograms, box plots, scatter plots, and bar charts.
• Descriptive statistics summarizes and describes a set of data, while inferential statistics make inferences and draw a conclusion about a large population from the sample of data.
• Inferential statistics may involve multivariate variables test of significance, whereas descriptive statistics typically involves univariate or bivariate variables.
• Descriptive statistics uses measures of central tendency and variability, while inferential statistics uses tests of significance such as t-test, ANOVA, regression analysis, and chi-square test.

Conclusion

Descriptive and inferential statistics are classifications of statistics. Descriptive statistics involves describing and summarizing the data. And inferential statistics involves making inferences about the population based on the sample data. Descriptive and inferential statistics are necessary for data analysis, complementing each other.

This article briefly covers what descriptive and inferential statistics are and the difference between them. The article also contains examples that help to understand the concepts better.

Hope you will like the article.

Happy Learning!!

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