I'm analyzing a cross-tabulation of two categorical variables (treatment group x outcome success/failure). Help me: 1) Create a contingency table with frequencies and percentages

Prompt
I'm analyzing a cross-tabulation of two categorical variables (treatment group x outcome success/failure). Help me: 1) Create a contingency table with frequencies and percentages
ChatGPTOpenAI
I'm analyzing a cross-tabulation of two categorical variables (treatment group x outcome success/failure). Help me: 1) Create a contingency table with frequencies and percentages

Creating a contingency table with frequencies and percentages is a great way to analyze the relationship between two categorical variables, such as treatment group and outcome (success/failure). Here’s a step-by-step guide on how to do this.

Step 1: Gather Your Data


First, you need to gather your data, which might look something like this:

Treatment GroupSuccessFailure
A3010
B2020
C2515

Step 2: Create the Contingency Table


  • Input the Frequencies: Create a table that shows the raw counts of successful and failed outcomes for each treatment group.


Treatment GroupSuccessFailureTotal
A301040
B202040
C251540
Total7545120

  • Calculate Percentages: You can calculate the percentage of successes and failures within each treatment group, and/or the overall percentage. There are two common approaches:

  • Row percentages (percentage of each outcome within each treatment group).

  • Column percentages (percentage of each treatment group within each outcome).


Here’s how to calculate row percentages (the percentage of successes/failures within each treatment group):
  • Success Percentage for A = (30 / 40) * 100 = 75%

  • Failure Percentage for A = (10 / 40) * 100 = 25%

  • Success Percentage for B = (20 / 40) * 100 = 50%

  • Failure Percentage for B = (20 / 40) * 100 = 50%

  • Success Percentage for C = (25 / 40) * 100 = 62.5%

  • Failure Percentage for C = (15 / 40) * 100 = 37.5%


Step 3: Final Contingency Table with Frequencies and Percentages

Treatment GroupSuccess (n)Success (%)Failure (n)Failure (%)Total (n)Total (%)
A3075%1025%4033.3%
B2050%2050%4033.3%
C2562.5%1537.5%4033.3%
Total75100%45100%120100%

Summary


  • The first column refers to the treatment groups.

  • The n columns reflect the frequencies (raw counts).

  • The percentages show the proportion of each outcome in prevalence.

  • The total row and column summarize overall counts and percentages.


This table allows you to easily compare the success and failure rates across treatment groups. If you have specific numbers or data in mind, feel free to provide them, and I can help you set up that exact table!

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MistralMistral AI
I'm analyzing a cross-tabulation of two categorical variables (treatment group x outcome success/failure). Help me: 1) Create a contingency table with frequencies and percentages

Sure, I can help you create a contingency table with frequencies and percentages. Let's assume you have two categorical variables:

  • Treatment Group: This could be, for example, "Control" and "Experimental".

  • Outcome: This could be "Success" and "Failure".


Here's a step-by-step guide to creating a contingency table with frequencies and percentages:

Step 1: Collect the Data


First, you need to collect the data. Let's assume you have the following data:
  • Control Group: 30 successes, 20 failures

  • Experimental Group: 40 successes, 10 failures


Step 2: Create the Contingency Table with Frequencies

SuccessFailureTotal
Control302050
Experimental401050
Total7030100

Step 3: Calculate Row Percentages


Row percentages show the distribution of outcomes within each treatment group.

SuccessFailureTotal
Control30 (60%)20 (40%)50 (100%)
Experimental40 (80%)10 (20%)50 (100%)
Total7030100

Step 4: Calculate Column Percentages


Column percentages show the distribution of treatment groups within each outcome.

SuccessFailureTotal
Control30 (42.9%)20 (66.7%)50
Experimental40 (57.1%)10 (33.3%)50
Total70 (100%)30 (100%)100

Step 5: Calculate Overall Percentages


Overall percentages show the distribution of each cell relative to the total number of observations.

SuccessFailureTotal
Control30 (30%)20 (20%)50 (50%)
Experimental40 (40%)10 (10%)50 (50%)
Total70 (70%)30 (30%)100 (100%)

Summary


  • Row Percentages: Show the distribution of outcomes within each treatment group.

  • Column Percentages: Show the distribution of treatment groups within each outcome.

  • Overall Percentages: Show the distribution of each cell relative to the total number of observations.


You can choose which type of percentage to include based on what you want to highlight in your analysis. If you provide specific data, I can help you calculate the exact percentages.

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Analysis

This comparison demonstrates the different approaches each AI model takes when responding to the same prompt. Here are the key differences observed:

Response Characteristics

ChatGPT: Provides a detailed response with 21 sentences.

Mistral: Provides a detailed response with 18 sentences.

Key Takeaways

  • Each model brings unique strengths to this type of query
  • Response styles vary significantly between models
  • Consider your specific use case when choosing between these models

Try This Comparison Yourself

Want to test these models with your own prompts? Visit SNEOS.com to compare AI responses side-by-side in real-time.

This comparison was generated using the SNEOS AI Comparison ToolPublished: October 15, 2025 | Models: ChatGPT, Mistral