Intro to Stats - Exam 2 Vocabulary
1. Define the field of Statistics
The science of collecting, analyzing, and interpreting data.
2. Define population.
The overall group about which you have interest.
3. Define sample.
A subset of the population.
4. Define random sample.
A sample where all members of the population have the same chance of being included.
5. Define representative sample.
A sample that has makeup consistent with the population.
6. Define parameter and statistic.
A parameter is a numerical characteristic of a population, and a statistic is a numerical characteristic of a random sample.
7. What is the whole point of statistical inference?
To draw conclusions about a population based upon a sample (or about a parameter based on a statistic). Also, to see what statistics tell us about parameters (or samples tell us about populations). (Any one of these answers would be correct.)
8. In words, what is a standard deviation? (I’m not looking for the formula.)
On the average, how much a list of data deviates from the mean.
9. What does a z-score tell you? (I’m not looking for the formula.)
How many standard deviations above the mean a raw score is.
10. If I say that the raw score 75 is at the 60th percentile, what does that mean?
Sixty percent of the data falls below 75.
11. What is probability?
A measurement of the likelihood of the occurrence of some chance event.
12. Define outcome.
A possible result of a probability experiment.
13. Define sample space.
A collection of all possible outcomes.
14. Define event.
A subset of the sample space, or a collection of outcomes. (Either answer is correct.)
15. Define simple event.
An event that consists of exactly one outcome.
16. Between what values do probabilities fall?
0 and 1
17. Define a random variable.
A function that turns outcomes into numbers.
18. Define a pdf (probability distribution function)
A function that shows how the probabilities are distributed over the different values of a random variable.
19. What is a sampling distribution?
A pdf for a statistic.
20. What is an unbiased estimator?
A statistic is called unbiased for a parameter if the value we expect that statistic to give is the parameter.
The mean of the statistic's sampling distribution is the parameter.
21. Suppose I tell you that the interval of numbers from 77 to 83 is a 95% confidence interval for the mean µ of a population. Briefly explain what is meant by that confidence interval.
We are 95% sure that the interval [77, 83] surrounds the parameter µ.
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