Estimating with Uncertainty

Question 1

estimation (2)

Answer 1

• process of inferring a population parameter from sample data
• value of estimate is almost never exactly true due to chance

Question 2

sampling distribution (2)

Answer 2

• probability distribution of all the values for an estimate that we might have obtained when we sampled a population
• increasing sample size reduces the spread/variance of sampling distribution of an estimate, increasing precision

Question 3

standard error (2)

Answer 3

• of an estimate, is the SD of the estimate’s sampling distribution
• reflects the precision of an estimate: estimates with smaller SE are more precise and there is less uncertainty about the target parameter in the pop.

Question 4

confidence interval

Answer 4

• range of values surrounding the sample estimate that is likely to contain the population parameter

Question 5

95% confidence interval

Answer 5

• provides the most plausible range for the parameter

- values lying within the interval are most plausible and those outside are unlikely based on the data

Question 6

what is the 2SE “quick approximation” of the 95% CI

Answer 6

Y bar - 2SE to Y bar + 2SE

Question 7

how to correctly talk about confidence intervals

Answer 7

• we are 95% confident that the population mean lies within the 95% confidence interval

Question 8

error bars

Answer 8

• lines on a graph extending outward from the sample estimate to illustrate uncertainty about the value of the parameter being estimated

Question 9

error bars

Answer 9

• lines on a graph extending outward from the sample estimate to illustrate uncertainty about the value of the parameter being estimated

Question 10

pseudoreplication

Answer 10

• error that occurs when individual measurements are not independent, but are treated as if they are