
STAT 5.6 difference of 2 proportions
Flashcard
•
Mathematics
•
9th - 12th Grade
•
Practice Problem
•
Hard
+2
Standards-aligned
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15 questions
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1.
FLASHCARD QUESTION
Front
What is the difference of two proportions?
Back
The difference of two proportions refers to the comparison of the proportions of a certain characteristic in two different groups. It is calculated as p1 - p2, where p1 is the proportion from the first group and p2 is the proportion from the second group.
2.
FLASHCARD QUESTION
Front
What is a sampling distribution?
Back
A sampling distribution is the probability distribution of a statistic (like a sample mean or sample proportion) obtained from a large number of samples drawn from a specific population.
3.
FLASHCARD QUESTION
Front
What is the Central Limit Theorem?
Back
The Central Limit Theorem states that the sampling distribution of the sample mean (or sample proportion) will be approximately normally distributed if the sample size is sufficiently large, regardless of the population's distribution.
4.
FLASHCARD QUESTION
Front
What is the formula for the standard deviation of the difference of two proportions?
Back
The standard deviation of the difference of two proportions is calculated using the formula: sqrt[(p1(1-p1)/n1) + (p2(1-p2)/n2)], where p1 and p2 are the sample proportions and n1 and n2 are the sample sizes.
5.
FLASHCARD QUESTION
Front
What does it mean if the sample sizes are large enough?
Back
Sample sizes are considered large enough if both np and n(1-p) are greater than 5 for each group, which allows the sampling distribution of the difference in proportions to be approximately normal.
Tags
CCSS.HSS.IC.B.4
6.
FLASHCARD QUESTION
Front
What is a null hypothesis in the context of comparing two proportions?
Back
The null hypothesis states that there is no difference between the two population proportions (p1 = p2). It is the hypothesis that is tested in statistical analysis.
7.
FLASHCARD QUESTION
Front
What is a p-value?
Back
A p-value is the probability of obtaining a test statistic at least as extreme as the one observed, assuming that the null hypothesis is true. It helps determine the significance of the results.
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