Lesson 10

Statistical Significance versus Practical Significance

Big question

Can a result be statistically significant but not important?

Lesson progress

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Big question
Concept
Activity
Quiz

Learning objectives

  • Explain statistical significance versus practical significance in plain language.
  • Use critical value correctly in an interpretation.
  • Connect the lesson idea to a formula, graph, Python result, or real example.

Simple explanation

Statistical significance measures evidence against a null, while practical significance asks whether the magnitude matters.

Key terms

critical value
The cutoff from a reference distribution used in a rejection rule.
linear restriction
A hypothesis that imposes a linear equation on regression parameters.
unrestricted model
The model estimated without imposing the null restrictions.

Core formula

importance=magnitude,units,context,anduncertaintyimportance = magnitude, units, context, and uncertainty

Use plain-language interpretation before algebra.

Example

401K gives students a real-data setting for statistical significance versus practical significance. The lesson emphasizes inference mechanics and interpretation, not memorized output.

Interactive visual

StatisticalVsPracticalSignificanceCard

Original Module 4 visual for Statistical Significance versus Practical Significance.

wage_sample.csv

y variable

wage

The dependent variable. It is the outcome students want to explain.

x variable

education

The explanatory variable. It is used to describe changes in wage.

Live Python

Statistical Significance versus Practical Significance Python example

Statistical Significance versus Practical Significance Python example

Stdout

Run Python to see results here.

Status / stderr

Ready to run Python in your browser.

Line-by-line guide

  1. Line 1Load a Python library needed for data work or regression.
  2. Line 2Load a Python library needed for data work or regression.
  3. Line 3Load a Python library needed for data work or regression.
  4. Line 5Load the dataset into a pandas DataFrame.
  5. Line 6Create or update a Python object used in the analysis.
  6. Line 7Add an intercept column to the regression design matrix.
  7. Line 8Estimate an ordinary least squares regression.
  8. Line 9Create or update a Python object used in the analysis.
  9. Line 10Create or update a Python object used in the analysis.
  10. Line 11Create or update a Python object used in the analysis.
  11. Line 12Create or update a Python object used in the analysis.
  12. Line 13Create or update a Python object used in the analysis.
  13. Line 14Create or update a Python object used in the analysis.
  14. Line 15Create or update a Python object used in the analysis.
  15. Line 16Create or update a Python object used in the analysis.
  16. Line 17Run this Python instruction as part of the lesson workflow.
  17. Line 18Create or update a Python object used in the analysis.
  18. Line 19Display a result so students can inspect the output.
  19. Line 20Display a result so students can inspect the output.
  20. Line 21Display a result so students can inspect the output.

Python walkthrough

  1. 1Load the real dataset and keep the variables needed for the model.
  2. 2Estimate OLS with statsmodels.
  3. 3Compute the statistic, p-value, or confidence interval.
  4. 4Interpret the result with units, controls, and limitations.

Live notebook

Run this lesson as a notebook

Open an editable notebook cell-by-cell, run Python in the browser, and download the `.ipynb` file for later.

Open live notebookDownload .ipynbDownload .py

Related dataset

WAGE1

Estimated time

25 to 40 min

Packages

pandas, numpy, statsmodels, patsy

Expected output

A regression or inference table with coefficients, uncertainty, and short interpretation notes.

Learning goals

  • Load and inspect WAGE1.
  • Run the Python cells connected to Testing a Single Coefficient Against Zero.
  • Interpret the output using t tests and WAGE1.

Common errors

  • File not found: check that WAGE1.DTA is installed or use the course data folder.
  • Package import error: use the browser notebook first, then download for local Jupyter if your local packages differ.
  • Column name error: compare your variable names with the dataset variables listed for this notebook.

Dataset path helper

import pandas as pd

df = pd.read_stata("/data/WAGE1.DTA")
df.head()

Interactive activity

StatisticalVsPracticalSignificanceCard

Classify significance

Separate statistical evidence from practical importance.

401K

Inputs

Pick the next report ingredient

Try it yourself

Write one plain-English sentence explaining the main idea from this lesson.

Common mistakes

Check these before you move on.

A regression coefficient describes a pattern unless the assumptions or research design support a causal interpretation.

Quick quiz

What is the safest inference focus in Statistical Significance versus Practical Significance?

Quick quiz

Which reporting habit is most important in Statistical Significance versus Practical Significance?

Quick quiz

Why is CEOSAL2 a reasonable practice dataset here?

Key takeaway

Statistical Significance versus Practical Significance turns regression output into evidence only when the hypothesis, assumptions, and magnitude are stated clearly.

Download dataDownload resultsDownload interpretation