WEEK 7- 13.2 inferential statistics

Question 1

inferential statistics

Answer 1

making inferences about a population using samples

Question 2

key assumptions of inferential statistics

Answer 2

1. sampling from complete target population
2. simple random sampling with perfect response rate (or non response completely random)
3. no nonsampling erorr

Question 3

population distribution

Answer 3

age of citizens, attitudes
usually unknown bc we can’t measure everyone

Question 4

sample distribution

Answer 4

we can see distribution in sample

Question 5

sampling distribution

Answer 5

multiple samples
a probabilty distribution

Question 6

normal distribution

Answer 6

tells you the probability of something being found by chance

Question 7

estimation and inferences

Answer 7

we want to draw inferences to a populaiton, so we get a sample, and caldulate statistics, and we make an inference to the population

Question 8

how do we know if our sample is good?

Answer 8

if we do multiple samples, and we calculate a sample statistic- these form a sampling distribution. we can calculate the standard error- allows us to say something about our confidence (CONFIDENCE THAT WE’RE CLOSE)

Question 9

central limit theorem

Answer 9

**when drawing an infinite number of random samples, the distribution of sample means will be normal **(even if hte original variable isn’t normal)

Question 10

confidence interval

Answer 10

we are 95% certain that our sample estimate comes close to the population parameter

Question 11

h0: no difference statistical significance testing

Answer 11

0 means no difference
if 0 is in the confidence interval
NO DIFFERENCE
CHANCE/ RANDOM

Question 12

observed difference testing

Answer 12

0 means no difference
if 0 is outside the confidence interval
UNLIKELY TO BE RANDOM

Question 13

small sample => large random eror =>

Answer 13

not statistically significant

Question 14

large sample => small random error =>

Answer 14

highly statistically significant