CHI-SQUARE TEST EXPLANATION 
Let’s say you want to know if there is a difference in the proportion of men and women who are left handed and 
let’s say in your sample 10% of men and 5% of women were left -handed. 
How it’s Calculated (Without the gory details) 
1. You collect the data. For example, you ask 120 men and 140 women which hand they use and get this: 
ACTUAL DATA Left-handed Right-handed 
Men 12 108 
Women 7 133 
2. Calculate what numbers of left and right-handers we would expect IF men and women were the same. 
In this case, IF men and women were equally left and right handed, we would have expected these numbers in 
our sample of 260 people (Ask if you want to know how this is done): 
EXPECTED IF NO DIFFERENCE Left-handed Right-handed 
Men 8.77 111.23 
Women 10.23 129.77 
3. The computer calculates a Chi-square (pronounced Ki-square) value. The Chi-square value is a single number 
that adds up all the differences between our actual data and the data expected if there is no difference. If the 
actual data and expected data (if no difference) are identical, the Chi -square value is 0. A bigger difference will 
give a bigger Chi-square value. 
4. Look up the Chi-square value in a table to see if it is big enough to indicate a significant difference in 
handedness of males and females. 
Interpretation 
Greater differences between expected and actual data produce a larger Chi -square value. The larger the Chi-square 
value, the greater the probability that there really is a significant difference. 
With a 2 by 2 table like this (If you have more than 4 cells of data in your table, see your instructor): 
If the Chi-square value is greater than or equal to the critical value 
There is a significant difference between the groups we are studying. That is, the difference between actual data 
and the expected data (that assumes the groups aren’t different) is probably too great to be attributed to chance. 
So we conclude that our sample supports the hypothesis of a difference. 
If the Chi-square value is less than the critical value 
There is no significant difference. The amount of difference between expected and actual data is likely just due to 
chance. Thus, we conclude that our sample does not support the hypothesis of a difference. 
In this example, the critical value is 3.8. The Chi-square value was 2.383, which is less than 3.8. Thus, there is 
no significant difference in handedness between men and women in our sample. We conclude that based on this 
sample, men and women in general seem equally likely to be left or right handed. 
**Warning** 
We have not proven anything!!! These first samples might be atypical. Repeated sampling may show a 
significant difference, or eliminate the difference we thought we saw. Because of this uncertainty, we can only 
say that the hypothesis was supported or not supported.
Graphing Categorical Data that you analyze with the Chi -Square Statistic 
Let’s say you want to know if there is a difference in the proportion of men and women who are left handed and 
you collect this data: 
ACTUAL DATA Left-handed Right-handed 
Men 12 108 
Women 7 133 
Calculate the % of men that are left-handed and right handed in your study: 
12 / 120 total men = 0.10 X 10 = 10% of men were left -handed 
108 / 120 = 0.90 X 100 = 90% of men were right-handed 
Do the same for women: 
7 / 140 total women = 0.05 X 100 = 5% left handed 
133 / 140 = 0.95 X 100 = 95% right-handed 
Plot these percents as bars: 
Percent 
Lef t 
Hand ed 
30 
20 
10 
Men Women 
Or plot as stacked bars: 
10 0 
Percent 
50 
Or plot as a pie diagram. 
Lef t- handed 
Rig ht -handed 
Men Women

More Related Content

PDF
Data Journalism - Newsroom Statistics
PPTX
Nature of the data (spread)
PPTX
Nature of the data (descriptive)
PDF
Bio-statistics definitions and misconceptions
PPTX
Bayeasian inference
PPTX
Infernetial vs desctiptive (jejit + indepth)
PPTX
Boolean Operators Explained
PPTX
WF ED 540, Class Meeting 8, 15 October 2015, contingency tables
Data Journalism - Newsroom Statistics
Nature of the data (spread)
Nature of the data (descriptive)
Bio-statistics definitions and misconceptions
Bayeasian inference
Infernetial vs desctiptive (jejit + indepth)
Boolean Operators Explained
WF ED 540, Class Meeting 8, 15 October 2015, contingency tables

Similar to Chi sq explanation (20)

DOCX
Page 266LEARNING OBJECTIVES· Explain how researchers use inf.docx
PDF
Hypothesis testing - Primer
PDF
Hypothesis test
PDF
Chapter8 introduction to hypothesis testing
PDF
40007 chapter8
PDF
Introduction to Hypothesis Testing LEARNING OBJECTIVESChapter8.pdf
PPTX
Steps in hypothesis.pptx
PPT
Data Analysis for Graduate Studies Summary
DOCX
Topic Learning TeamNumber of Pages 2 (Double Spaced)Num.docx
PPTX
Chi-Square Test of Independence
PDF
Prob ^0 Stats Proj 1
PPTX
Math class 8 data handling
PPT
Frequency Tables - Statistics
DOCX
Hypothesis testing
PDF
Determination and Analysis of Sample size
PPTX
How to calculate power in statistics
PPTX
data handling class 8
PDF
analysing_data_using_spss.pdf
PDF
Analysis Of Data Using SPSS
PDF
analysing_data_using_spss.pdf
Page 266LEARNING OBJECTIVES· Explain how researchers use inf.docx
Hypothesis testing - Primer
Hypothesis test
Chapter8 introduction to hypothesis testing
40007 chapter8
Introduction to Hypothesis Testing LEARNING OBJECTIVESChapter8.pdf
Steps in hypothesis.pptx
Data Analysis for Graduate Studies Summary
Topic Learning TeamNumber of Pages 2 (Double Spaced)Num.docx
Chi-Square Test of Independence
Prob ^0 Stats Proj 1
Math class 8 data handling
Frequency Tables - Statistics
Hypothesis testing
Determination and Analysis of Sample size
How to calculate power in statistics
data handling class 8
analysing_data_using_spss.pdf
Analysis Of Data Using SPSS
analysing_data_using_spss.pdf
Ad

More from Chinnannan Periasamy (15)

PDF
Inventory notes
DOCX
Qtmd syllabus
DOCX
Ec 15 101 advanced engineering mathematics
PPTX
Fourier transform
DOCX
PPTX
Parametric test
DOCX
HYPOTHESES STEPS
DOCX
11.00 technology ok
PPT
segmentation
PPTX
1.11 functions of money ppt-ok
PPTX
DOCX
M tech regulations
PPTX
Inventory notes
Qtmd syllabus
Ec 15 101 advanced engineering mathematics
Fourier transform
Parametric test
HYPOTHESES STEPS
11.00 technology ok
segmentation
1.11 functions of money ppt-ok
M tech regulations
Ad

Recently uploaded (20)

PDF
Improvisation in detection of pomegranate leaf disease using transfer learni...
PDF
Architecture types and enterprise applications.pdf
PDF
The influence of sentiment analysis in enhancing early warning system model f...
PPT
Module 1.ppt Iot fundamentals and Architecture
PDF
A contest of sentiment analysis: k-nearest neighbor versus neural network
PDF
Developing a website for English-speaking practice to English as a foreign la...
PPTX
AI IN MARKETING- PRESENTED BY ANWAR KABIR 1st June 2025.pptx
PDF
“A New Era of 3D Sensing: Transforming Industries and Creating Opportunities,...
PPTX
Chapter 5: Probability Theory and Statistics
PPT
Geologic Time for studying geology for geologist
PDF
Zenith AI: Advanced Artificial Intelligence
PDF
CloudStack 4.21: First Look Webinar slides
PPTX
2018-HIPAA-Renewal-Training for executives
PDF
Taming the Chaos: How to Turn Unstructured Data into Decisions
PDF
UiPath Agentic Automation session 1: RPA to Agents
PPTX
Build Your First AI Agent with UiPath.pptx
DOCX
search engine optimization ppt fir known well about this
PPTX
Benefits of Physical activity for teenagers.pptx
PPT
Galois Field Theory of Risk: A Perspective, Protocol, and Mathematical Backgr...
PDF
A review of recent deep learning applications in wood surface defect identifi...
Improvisation in detection of pomegranate leaf disease using transfer learni...
Architecture types and enterprise applications.pdf
The influence of sentiment analysis in enhancing early warning system model f...
Module 1.ppt Iot fundamentals and Architecture
A contest of sentiment analysis: k-nearest neighbor versus neural network
Developing a website for English-speaking practice to English as a foreign la...
AI IN MARKETING- PRESENTED BY ANWAR KABIR 1st June 2025.pptx
“A New Era of 3D Sensing: Transforming Industries and Creating Opportunities,...
Chapter 5: Probability Theory and Statistics
Geologic Time for studying geology for geologist
Zenith AI: Advanced Artificial Intelligence
CloudStack 4.21: First Look Webinar slides
2018-HIPAA-Renewal-Training for executives
Taming the Chaos: How to Turn Unstructured Data into Decisions
UiPath Agentic Automation session 1: RPA to Agents
Build Your First AI Agent with UiPath.pptx
search engine optimization ppt fir known well about this
Benefits of Physical activity for teenagers.pptx
Galois Field Theory of Risk: A Perspective, Protocol, and Mathematical Backgr...
A review of recent deep learning applications in wood surface defect identifi...

Chi sq explanation

  • 1. CHI-SQUARE TEST EXPLANATION Let’s say you want to know if there is a difference in the proportion of men and women who are left handed and let’s say in your sample 10% of men and 5% of women were left -handed. How it’s Calculated (Without the gory details) 1. You collect the data. For example, you ask 120 men and 140 women which hand they use and get this: ACTUAL DATA Left-handed Right-handed Men 12 108 Women 7 133 2. Calculate what numbers of left and right-handers we would expect IF men and women were the same. In this case, IF men and women were equally left and right handed, we would have expected these numbers in our sample of 260 people (Ask if you want to know how this is done): EXPECTED IF NO DIFFERENCE Left-handed Right-handed Men 8.77 111.23 Women 10.23 129.77 3. The computer calculates a Chi-square (pronounced Ki-square) value. The Chi-square value is a single number that adds up all the differences between our actual data and the data expected if there is no difference. If the actual data and expected data (if no difference) are identical, the Chi -square value is 0. A bigger difference will give a bigger Chi-square value. 4. Look up the Chi-square value in a table to see if it is big enough to indicate a significant difference in handedness of males and females. Interpretation Greater differences between expected and actual data produce a larger Chi -square value. The larger the Chi-square value, the greater the probability that there really is a significant difference. With a 2 by 2 table like this (If you have more than 4 cells of data in your table, see your instructor): If the Chi-square value is greater than or equal to the critical value There is a significant difference between the groups we are studying. That is, the difference between actual data and the expected data (that assumes the groups aren’t different) is probably too great to be attributed to chance. So we conclude that our sample supports the hypothesis of a difference. If the Chi-square value is less than the critical value There is no significant difference. The amount of difference between expected and actual data is likely just due to chance. Thus, we conclude that our sample does not support the hypothesis of a difference. In this example, the critical value is 3.8. The Chi-square value was 2.383, which is less than 3.8. Thus, there is no significant difference in handedness between men and women in our sample. We conclude that based on this sample, men and women in general seem equally likely to be left or right handed. **Warning** We have not proven anything!!! These first samples might be atypical. Repeated sampling may show a significant difference, or eliminate the difference we thought we saw. Because of this uncertainty, we can only say that the hypothesis was supported or not supported.
  • 2. Graphing Categorical Data that you analyze with the Chi -Square Statistic Let’s say you want to know if there is a difference in the proportion of men and women who are left handed and you collect this data: ACTUAL DATA Left-handed Right-handed Men 12 108 Women 7 133 Calculate the % of men that are left-handed and right handed in your study: 12 / 120 total men = 0.10 X 10 = 10% of men were left -handed 108 / 120 = 0.90 X 100 = 90% of men were right-handed Do the same for women: 7 / 140 total women = 0.05 X 100 = 5% left handed 133 / 140 = 0.95 X 100 = 95% right-handed Plot these percents as bars: Percent Lef t Hand ed 30 20 10 Men Women Or plot as stacked bars: 10 0 Percent 50 Or plot as a pie diagram. Lef t- handed Rig ht -handed Men Women