Sampling Distributions for Differences in Sample Proportions

Jackson Hernandez
9 min read
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Study Guide Overview
This study guide covers comparing two proportions, focusing on calculating the standard deviation of the difference. It emphasizes the importance of adding variances, using the formula (provided), and checking the Large Counts Condition. It also reviews sampling distributions, the Central Limit Theorem (CLT), common mistakes, and includes practice problems and exam tips covering confidence intervals and hypothesis tests.
#AP Statistics: Differences in Proportions - The Night Before ๐
Hey! Let's get you ready for the AP Stats exam. We're focusing on comparing proportions today, a key area where you can really shine. Remember, it's all about understanding the why behind the formulas, not just memorizing them. Let's dive in!
#Comparing Two Proportions
#Differences (Non-Distribution) Recap
When we're dealing with differences in sample proportions or means, remember this golden rule: variances ALWAYS add. โ This is crucial! If you need the standard deviation, just take the square root of the combined variance. For means, you can subtract them directly, but variances always add. It's a little quirky, but that's statistics for you! ๐
#Proportion Differences
When comparing two proportions, we're essentially looking at the difference between two sample groups. Here's how to find the standard deviation of that difference:
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Variance Addition: If you have standard deviations, square them first to get variances. Then, add these variances. ๐ This is often called the "Pythagorean Theorem of Statistics" because it looks like the Pythagorean theorem, but it's for variances.
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Standard Deviation: Take the square root of the sum of the variances to get the standard deviation of the difference.
Here's the formula, straight from the AP Stats formula sheet:

*Caption: The formula for the standard deviation of the difference between two sample proportions. Notice how the variances are added before taking the square root.*

*Caption: Another view of the standard deviation formula. Remember, this is for the sampling distribution of the difference in proportions.*
Large Counts Condition: For any proportion inference, you MUST check the Large Counts condition to confirm normality. This means verifying that:
- Remember, the Central Limit Theorem (CLT) only applies to means (quantitative data), not proportions (ca...

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