Lectures 4-6

Question 1

What do inferential statistics quantify?

Answer 1

difference between two sample means = determine between two possible explanations

Question 2

what is the probability of obtaining your sample data and statistics from a population in which the null hypothesis is true (no difference)

Answer 2

(α) p-value

Question 3

large p-value

Answer 3

small difference between the means, null hypothesis = high probability of occurring

Question 4

small p-value

Answer 4

large difference between the means, null hypothesis = small probability of occurring (accept HA

Question 5

when is the null hypothesis rejected?

Answer 5

at p-value = 0.05 (significance level)

Question 6

what type of error occurs when reject H0 when it was true

Answer 6

Type I error

Question 7

what type of error occurs when accept H0 when it was false

Answer 7

Type II error

Question 8

what represents the probability of making a type II error

Answer 8

beta probability β | β ≠ 1 - α where a=0.05

Question 9

Inverse relationship between type I error and type II error

Answer 9

as the chance of making a type I error is decreased, chances of making a type II error are increased = why significance level is at 0.05

Question 10

goodness-of-fit test

Answer 10

1-nominal-scale variable where the frequency is compared to an a priori ratio

Question 11

what does the goodness-of-fit test analyze

Answer 11

the frequencies of occurrence within each of the categories of the variable

Question 12

a pre-determined, already-established distribution

Answer 12

a priori ratio

Question 13

(goodness-of-fit test) H0

Answer 13

there is NO DIFFERENCE between observed and expected frequencies

Question 14

(goodness-of-fit test) HA

Answer 14

data does not match a priori ration – there are differences between observed and expected

Question 15

test statistic

Answer 15

finds p-value || calculated to determine the probability of the observed frequencies conforming to the distribution established by the expected frequencies

Question 16

(goodness-of-fit test) degrees of freedom

Answer 16

k - 1 where k = # of categories

Question 17

Roscoe & Byar’s rule (R&B rule)

Answer 17

must be met to determine if the sample size (n) is large enough

Question 18

(goodness-of-fit test) and (contingency tables) testing for continuity

Answer 18

DF (k-1) must = 1

Question 19

contingency tables

Answer 19

2-nominal-scale variables || analyze frequencies of occurrence within each of the categories of both (i.e.: male/female) variables

Question 20

what do the contingency tables test for?

Answer 20

they DETERMINE if the frequency of occurrence of the categories of one variable is INDEPENDENT of the other

Question 21

expected frequencies between contingency tables vs. goodness-of-fit tests

Answer 21

goodness-of-fit = a priori expected values || contingency tables = a posteriori expected values

Question 22

what is term to describe when variables obtained are BASED on data (i.e.:via calculations)

Answer 22

a posteriori

Question 23

(contingency tables) degrees of freedom

Answer 23

(r-1)(c-1)

Question 24

multidimensional contingency tables

Answer 24

deal with 3 or more nominal-scale variables