HYPOTHESIS TESTING, COMPARING MEANS

Question 1

data

Answer 1

pieces of info collected in study

Question 2

variable

Answer 2

measurement which varies btw subjects, e.g. height

Question 3

2 types of data

Answer 3

categorical and numerical values

Question 4

categorical values

Answer 4

can be sorted into categories/groups, represented by bar charts and pie graphs

Question 5

numerical values

Answer 5

can be observed/measured, numbers placed in ascending/descending order, represented by line graphs and scatter plots

Question 6

types of catagorical data

Answer 6

nominal and ordinal

Question 7

nominal values

Answer 7

assigned a ‘label’ in the form of numbers, e.g. sex

can be counted but not ordered

Question 8

ordinal values

Answer 8

can be counted and ordered/ have a rating scale attached, but not measured, e.g. house numbers, swimming level

Question 9

samples

Answer 9

subset of larger data, used to draw inferences about larger set (population)

Question 10

parameters

Answer 10

characteristics of population data

Question 11

average used for normally distributed nominal data

Answer 11

mean

MoS= standard deviation

Question 12

average used for skewed nominal data

Answer 12

median

MoS= interquartile range

Question 13

average used for ordinal categorical data

Answer 13

median

MoS= interquartile range

Question 14

average used for nominal categorical data

Answer 14

mode

MoS= none

Question 15

Alternative hypothesis (Ha)

Answer 15

what we aim to gather evidence of

that there is a difference/relationship etc

Question 16

Null hypothesis (H0)

Answer 16

what we assume is true to begin with

there is no difference/relationship etc

Question 17

when can null be rejected

Answer 17

only if there is enough evidence to doubt it

Question 18

type 1 error

Answer 18

Incorrect rejection of null hypothesis

e.g. study shows there is a difference but the difference does not exist in population

Question 19

type 2 error

Answer 19

Failure to reject null hypothesis, when alternative hypothesis is true
e.g. study shows no difference but there is a difference in the population

Question 20

t-tests

Answer 20

used to compare 2 population means

Question 21

paired data

Answer 21

same individual studied at 2 different times/under 2 different conditions, use paired t-test

Question 22

independent data

Answer 22

data collected from 2 separate groups, use independent samples test

Question 23

one way analysis of variance btw groups

Answer 23

when you want to test the difference between 2 groups

Question 24

two way analysis of variance without replication

Answer 24

double-test the same group, e.g. test a group before/after they take medication

Question 25

two way analysis of variance with replication

Answer 25

test two groups on more than one thing, e.g. 2 groups of patients trying 2 diff therapies

Question 26

one way analysis of variance

Answer 26

compares 2 means from 2 independent groups
looks at variation within and between groups
null hypothesis= 2 means are equal
significant result= 2 means are unequal

Question 27

limitations of one way ANOVA

Answer 27

will tell you groups are different but wont tell you what groups

Question 28

two way analysis of variance

Answer 28

two independents

results calculate main effect & interaction effect

Question 29

assumptions for two way analysis of variance

Answer 29

population must be close to normal distribution
samples must be independent
population variances must be equal
groups must have equal sample size

Question 30

homogeneity of variance

Answer 30

each group should have similar standard deviation