What is the difference between descriptive and inferential statistics?

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Descriptive Statistics

  • Definition: Descriptive statistics summarize and present the main features of a dataset in a simple, understandable way.

  • Purpose: To describe what the data shows.

  • Methods/Measures: Mean, median, mode, standard deviation, variance, percentages, charts, and graphs.

  • Scope: Works only with the data you have; it doesn’t make predictions or generalizations.

  • Example: If you surveyed 100 students and found their average exam score was 75, that’s descriptive statistics.

Inferential Statistics

  • Definition: Inferential statistics use data from a sample to make conclusions, predictions, or generalizations about a larger population.

  • Purpose: To draw inferences beyond the data you directly have.

  • Methods/Measures: Hypothesis testing, confidence intervals, regression analysis, ANOVA, probability models.

  • Scope: Goes beyond the sample to estimate or test assumptions about the whole population.

  • Example: Using the sample of 100 students to predict the average exam score of all students in the school.

Key Differences in One Look

AspectDescriptive StatisticsInferential Statistics
FocusSummarizes existing dataMakes predictions/generalizations
GoalDescribe the datasetInfer about the population
Data RequiredWhole dataset or sampleSample (to estimate population)
TechniquesAverages, percentages, graphsHypothesis tests, regression, probability
Example“The average height of this group is 170 cm.”“The average height of all adults in the country is likely 170 cm.”

✅ In summary:

  • Descriptive = Describe what is

  • Inferential = Predict or infer what could be

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