Descriptive vs. Inferential Statistics

Statistical analysis in analytics typically falls into two broad categories: Descriptive Statistics and Inferential Statistics. Understanding their differences is essential before applying them to any dataset or business problem.

Descriptive Statistics: Summarizing the Data You Have

Descriptive statistics are used to organize, summarize, and describe the features of a specific dataset. This includes raw numerical data, trends, and visual summaries.

Key Characteristics:

• Works on entire datasets (no generalization).
• Helps understand data patterns at a glance.
• Often used in dashboards, reports, and summaries.

Common Descriptive Tools:

• Measures of Central Tendency: Mean, Median, Mode.
• Measures of Dispersion: Range, Variance, Standard Deviation.
• Visual Tools: Histograms, Bar Charts, Box Plots.

Example:

If Zomato wants to know the average delivery time of restaurants in Delhi based on all orders last month, descriptive statistics will provide that summary.

Inferential Statistics: Making Predictions or Decisions

Inferential statistics are used when you want to draw conclusions or make predictions about a larger population based on a sample of data.

Key Characteristics:

• Works on samples, not full datasets.
• Involves probability, estimation, and hypothesis testing.
• Commonly used when it’s not practical to collect data from every subject.

Common Inferential Methods:

• Confidence Intervals
• Hypothesis Tests (e.g., t-tests, chi-square tests)
• Regression Analysis
• ANOVA

Example:

If YouTube wants to predict average watch time per user across India by analyzing only 1,000 user accounts, it uses inferential statistics to generalize findings from the sample.

Comparison Table