Uncertainty, standard errors and confidence intervals

Question 1

How could you infer what the population distribution could look like from a sample?
1. Collect a ____ sample
2. Measure the ____ in our sample on some ____
3. Calculate the ____ and ____ of the ____ and use to ____ what the population ____ could look like

Answer 1

1. random
2. individuals, variable
3. mean, SD, sample, infer, distribution

Question 2

What are two tools we use to quantify uncertainty around how close the sample mean is to the true population value?

Answer 2

Standard errors and confidence intervals

Question 3

What is meant by an estimate?

Answer 3

Can be different things in different contexts
e.g difference in group means, measure of association

Question 4

What three things can we describe every normal distribution using?

Answer 4

1. Mean (central value)
2. Standard Deviation (average difference from the mean)
3. Proportions of scores at cut-off points (~68% scores within 1 SD of mean, 95% of scores within ± 1.96 CDs of mean)

Question 5

What is the difference between standard deviation and standard error?
SD = the ____ difference between each ____ and the ____ mean
SE = the average ____ between each ____ mean and the ____ value

Answer 5

SD = average, score, sample
SE = difference, sample,
population

Question 6

How do you calculate standard error?
Standard error = sample ____ ____ / √ the ____ ____

Answer 6

Standard error = sample standard deviation / √ the sample size

Question 7

The t-distribution is defined by ____ of ____ - calculated as ________

Answer 7

Degrees of freedom - calculated as N-1

Question 8

How do we interpret confidence levels?
____ that our sample is one of the ____ producing confidence intervals that contain the ____ value, then the ____ value for the ____ of interest falls somewhere between the ____ limit and the ____ limit of the ____ we’ve computed for our ____

Answer 8

ASSUMING
95%
population
population
estimate
lower
upper
interval
sample