Questions and Answers
What is the standard deviation of averages?
The standard deviation divided by the square root of the mean.
The central limit theorem states that as the sample size increases, the sampling distribution will become more and more average.
True
The central limit theorem describes the properties of standard normal distributions.
False
A sampling distribution is a distribution of samples instead of individuals.
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The central limit theorem states that the mean of the sample averages is equal to the mean of the individual values.
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Coincidental evidence is an isolated case that is cited to support a claim.
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Study Notes
Standard Deviation and Sampling
- Standard deviation of averages = standard deviation / √(sample size).
- Important for understanding variability in sample means.
Central Limit Theorem (CLT)
- As sample size increases, sampling distribution approaches a normal distribution regardless of the population's distribution.
- Sample mean averages become more consistent and "average" as the size grows.
- The mean of the sample averages equals the mean of the individual values, establishing a critical relationship in statistics.
Sampling Distributions
- A sampling distribution consists of statistics (like means) calculated from multiple samples rather than from individual data points.
- Helps understand the behavior of estimates derived from random samples.
Coincidental Evidence
- Coincidental evidence refers to isolated cases used to back a claim but lacks broader applicability.
- Often misleading; properly categorized as anecdotal evidence, which is not scientifically valid.
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Description
Test your knowledge of Algebra 2 concepts with these flashcards. Focus on the standard deviation, central limit theorem, and properties of standard normal distributions. Each card presents a term with its definition for effective learning.
