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Research Design Human Movement 2032 > WEEK #4(b) - research methods > Flashcards

WEEK #4(b) - research methods Flashcards

1
Q

what is significance testing based on ?

A

the statistical properties of sample data

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2
Q

what can we extrapolate “estimate” with significance testing ?

A

the probability of the observed differences or relationships occurring in the target population

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3
Q

what are we assuming in regards to significance testing ?

A

that the sample data is representative and that that data meets the assumptions associated with the inferential test

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4
Q

who developed significance testing ?

A

Ronald Fisher (1920s-1930s)

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5
Q

TRUE OR FALSE

agricultural research designs couldn’t be fully experimental because natural variations such as weather and soil quality couldn’t be fully controlled

A

TRUE

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6
Q

what are some criticisms of significance testing ?

A
  • the null hypothesis is rarely true
  • no clinical effect / no change
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7
Q

what does significance testing provide :

A
  • a binary decision (yes or no)
  • direction of the effect
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8
Q

what does statistical significance simply mean ?

A

that the observed effect (relationship or differences) are unlikely to be due to sampling error

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9
Q

TRUE OR FALSE

statistical significance can be evident for very small (trivial) effects if N and/or critical alpha are large enough

A

TRUE

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10
Q

what does level of significance (aka alpha) represent ?

A

the probability of obtaining your results due to chance

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11
Q

the smaller the value is, what does this mean for level of significance (alpha) ?

A

the more “unusual” the results, indicting that the sample is from a different population than its being compared to (for example)

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12
Q

what happens if p-values fall below significance level for statistical significance alpha ?

A

we say that the results from the test are statistically significant

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13
Q

what happens if p > a ?

A

then FAIL TO REJECT the null hypothesis

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14
Q

what happens if p < a ?

A

then REJECT the null hypothesis

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15
Q

TRUE OR FALSE

alpha does not represent your chance of making a type 1 error

A

FALSE

alpha DOES represent your chance of making a type 1 error

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16
Q

define power beta :

A

refers to your study’s ability to find a difference if there is one

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17
Q

the greater the power, what does this mean ?

A

the more meaningful your results are

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18
Q

what does beta = ?

A

1 - power

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19
Q

FILL IN THE BLANK

beta represents the chance of making a __________

A

type 2 error

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20
Q

in regards to power beta what do you incorrectly fail to reject ?

A

the null

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21
Q

what is the desirable power of beta ?

A

desirable power > .80

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22
Q

what is the typical power of power beta ?

A

typical power around 60

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23
Q

when does power become higher ?

A

when there is an increase in sample size (N), critical alpha (a), and effect size (delta)

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24
Q

based on what can we calculate expected power before conducting a study ( a priori) :

A
  • estimated N
  • critical alpha
  • expected or minimum effect size )from related research)
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25
Q

what is effect size ?

A

a measure of the strength (size) of a relationship or effect

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26
Q

what is an inferential test ?

A

used as a way to quantify if there is an association, a difference or relationship between variables

statistical tests used to determine whether the research results can be generalised to the general populatio

27
Q

TRUE OR FALSE

an inferential test may be statistically significant, but this doesn’t indicate hw large the effect is

A

TRUE

28
Q

are effect sizes influenced by N ?

A

NO

unlike significance testing, effect sizes are not influenced by N

29
Q

is significance testing influenced by N ?

A

YES

30
Q

what is a common effect size statistic ?

A

Cohen’s d

31
Q

what is Cohen’s d ?

A

equal to the mean difference divided by the standard deviation

32
Q

when using Cohen’s d what are the three effect sizes ?

A

d = 0.2 small effect
d = 0.5 medium effect
d = 0.8 larger effect

33
Q

according to Cohen’s d, what is a small effect ?

A

d = 0.2

34
Q

according to Cohen’s d, what is a large effect ?

A

d = 0.8

35
Q

according to Cohen’s d, what is a medium effect ?

A

d = 0.5

36
Q

TRUE OR FALSE

p-value is the exact level

A

TRUE

37
Q

FILL IN THE BLANK

alpha is the maximum ___________ (max level)

A

type 1 error

38
Q

TRUE OR FALSE

type 1 error is the probability of type 1 error

A

TRUE

39
Q

what is type 1 error the probability of ?

A

rejecting a correct null hypothesis

40
Q

define a small p-value :

A
  • small enough p-values gives us reason t reject H0 and support HA
41
Q

what do p-values tell us exactly ?

A

how likely we are to make a type 1 error if we reject H0

42
Q

for p-values, what size is better ?

A

smaller is better (in support of alternative hypothesis)

43
Q

(in plain English) what is the p-value ?

A

the probability of incorrectly rejecting the null hypothesis OR the probability of rejecting a null hypothesis when in fact it is “true”

44
Q

FILL IN THE BLANK

_________ is the chance of error you will have to accept if you want to reject the null hypothesis

A

the p-value

45
Q

if a p-value is 0.01 what does this mean ?

A

there is a 1% chance that we will incorrectly reject the null hypothesis, or that we could reject the null hypothesis with a 1% chance of error

46
Q

if a p-value is 0.04 what does this mean ?

A

there is a 4% chance that we will incorrectly reject the null hypothesis, or that we could reject the null hypothesis with a 4% chance of error

47
Q

if the p-value is between 0 and 0.01, what does this imply ?

A

overwhelming evidence

48
Q

if the p-value is between 0.01 and 0.05, what does this imply

A

strong evidence

49
Q

if the p-value is between 0.05 and 0.10, what does this imply ?

A

weak evidence

50
Q

if the p-value is greater than 0.10, what does this mean ?

A

no evidence in favour of HA

51
Q

when talking about alpha and p how should you think of the alpha ?

A

think of the alpha as the minimum p-value you are willing to accept

52
Q

what are the three steps of alpha and p ?

A

1) set the alpha level before you calculate the p value
2) calculate the p-value
3) compare the p-value to the alpha level

53
Q

if p > a what does this mean ?

A

then FAIL TO REJECT the null hypothesis

54
Q

if p < a what does this mean ?

A

then REJECT the null hypothesis

55
Q

what are the two ways in which p-values can be reported ?

A
  • actual p-values (P = 0.04)
  • statement on inequality (P < 0.05)
56
Q

TRUE OR FALSE

most of the time, the null effect is true

A

FALSE

most of the time, the null effect is NOT true (impossible)

57
Q

what is the only way we can calculate probability for p-values misconceptions ?

A

Bayes Theorum

58
Q

what is the p value in null hypothesis significant testing condition on ?

A

the null hypothesis being true

59
Q

what does “the p value in null hypothesis significant testing condition on the null hypothesis being true” mean ?

A

that a p value of 0.05 does not mean that the probability our data arose by chance alone is 1 in 20

60
Q

FILL IN THE BLANK

the chance of us mistakenly rejecting the null hypothesis and concluding we have a successful treatment is more in the region of _________ %

A

30 - 60 %

61
Q

what is a p value problem ?

A

American statistical association argued, “by itself, a p value does not provide a good measure of evidence regarding a model hypothesis”

62
Q

FILL IN THE BLANK

the false positive rate associated with a p-value of .05 is usually around _____, but can be much higher

A

30%

63
Q

even when a p-value is interpreted correctly, what is it silent on ?

A

the magnitude an range of an effect

Research Design Human Movement 2032 (11 decks)

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