STATS quiz #2 Flashcards
Sampling Distribution
A probability distribution that specifies the possible outcomes of a sample statistic. Theoretical distribution of sample statistics from an infinite number of repeated random samples
Sampling Error
the discrepancy between a sample estimate of a population parameter and the real population parameter.
Standard Error
Measures the variability of a sampling distribution. The larger the sample size, the lower the standard error bc it’s closer to the mean.
Central Limit Theorem
If the sample size is large enough, then the distribution of sample means is approximately normal with a mean = u (population mean) and standard error (measure of dispersion)
Confidence Intervals
Basically MOE or margin of error. We calculate how far something is from the population mean.
Sampling is necessary because
because researchers in the social sciences rarely have enough resources to collect information about the entire set of subjects of interest to them.
Parameters are associated with X - statistics are associated with Y
populations and samples
T/F: The sampling distribution and distribution of the sample are the same thing.
F
The variability we expect to see from one random sample to another is called
Sampling Error
T/F: The Central Limit Theorem (CLT) states that the sampling distribution model of the sample mean (and proportion) from a random sample is approx. Normal for any n, regardless of the distribution of the population, as long as the observations are random
F
MOE
The amount of error above and below the point estimate of the population parameter caused by sampling variability (the product of the standard error and z or t)
Confidence interval
An interval estimate of a population parameter (propor- tion or mean) that covers a range of values
