HYPOTHESIS TESTING, COMPARING MEANS Flashcards

1
Q

data

A

pieces of info collected in study

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2
Q

variable

A

measurement which varies btw subjects, e.g. height

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3
Q

2 types of data

A

categorical and numerical values

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4
Q

categorical values

A

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

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5
Q

numerical values

A

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

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6
Q

types of catagorical data

A

nominal and ordinal

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7
Q

nominal values

A

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

can be counted but not ordered

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8
Q

ordinal values

A

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

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9
Q

samples

A

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

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10
Q

parameters

A

characteristics of population data

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11
Q

average used for normally distributed nominal data

A

mean

MoS= standard deviation

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12
Q

average used for skewed nominal data

A

median

MoS= interquartile range

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13
Q

average used for ordinal categorical data

A

median

MoS= interquartile range

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14
Q

average used for nominal categorical data

A

mode

MoS= none

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15
Q

Alternative hypothesis (Ha)

A

what we aim to gather evidence of

that there is a difference/relationship etc

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16
Q

Null hypothesis (H0)

A

what we assume is true to begin with

there is no difference/relationship etc

17
Q

when can null be rejected

A

only if there is enough evidence to doubt it

18
Q

type 1 error

A

Incorrect rejection of null hypothesis

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

19
Q

type 2 error

A

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

20
Q

t-tests

A

used to compare 2 population means

21
Q

paired data

A

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

22
Q

independent data

A

data collected from 2 separate groups, use independent samples test

23
Q

one way analysis of variance btw groups

A

when you want to test the difference between 2 groups

24
Q

two way analysis of variance without replication

A

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

25
two way analysis of variance with replication
test two groups on more than one thing, e.g. 2 groups of patients trying 2 diff therapies
26
one way analysis of variance
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
27
limitations of one way ANOVA
will tell you groups are different but wont tell you what groups
28
two way analysis of variance
two independents | results calculate main effect & interaction effect
29
assumptions for two way analysis of variance
population must be close to normal distribution samples must be independent population variances must be equal groups must have equal sample size
30
homogeneity of variance
each group should have similar standard deviation