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Research Design Human Movement 2032 > WEEK #4 - research method > Flashcards

WEEK #4 - research method Flashcards

1
Q

TRUEP OR FALSE

if everything is equal we should expect a normal curve

A

TRUE

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2
Q

TRUE OR FALSE

computers are better at random generated numbers

A

FALSE

computers are not good at random generated numbers

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3
Q

what are standard scores ?

A

indicates how many standard deviations a datum is above or below the population/sample mean

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4
Q

what are standard deviations ?

A

measure of the amount of variation of a random variable expected about its mean

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5
Q

when are standard scores useful ?

A

to describe data points on different measures, in terms of a common scale

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6
Q

the following is an example of what ?

“

A

standard scores

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7
Q

what is the easiest way to compare scores on a common scale ?

A

to use standard scores

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8
Q

what type of standard scores are most commonly used ?

A

z score or T score

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9
Q

between a T and z score which is more commonly used to compare scores on a common scale ?

A

T score

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10
Q

what is the z-score ?

A

indicates how much a given value differs from the standard deviation

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11
Q

what is the z-distribution ?

A
  • ranges from negative infinity to positive infinity
  • has a mean of 0
  • has a standard deviation of 1
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12
Q

TRUE OR FALSE

z-scores may NOT be thought of as “standard deviation units”

A

FALSE

z-scores MAY be thought of as “standard deviation units”

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13
Q

what is the t score ?

A

another way of standardizing scores by convention and is a standard score (like z), but without negative values

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14
Q

when do we use T-scores ?

A

to make numbers more valuable as their positive numbers

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15
Q

what are percentiles ?

A

refers to the proportion scoring less than a particular value

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16
Q

TRUE OR FALSE

we can also express z-scores as percentiles ?

A

TRUE

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17
Q

where are percentiles obtained by ?

A

a z-table

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18
Q

what do we obtain from a z-table ?

A

percentiles

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19
Q

YES OR NO

can we sample a population ?

A

NO we can’t

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20
Q

what do we look for during sample distribution ?

A
  • pretty close @ random sample
  • sample age
  • diversity
  • sex & gender
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21
Q

what are “alternative hypotheses” ?

A

alternative explanation from the status quo

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22
Q

what is “research hypotheses” ?

A

researchers true expectation of results

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23
Q

what is a “null hypothesis” ?

A
  • “the status quo”
  • comparison statement for research hypothesis
  • can be used to specify a particular threshold for an event
24
Q

what do “null hypothesis;” typically involve the assumption of ?

A
  • nothing has happened; or
  • no relationship exists; or
  • no change has occurred
25
Q

why is the point “can be used to specify a particular threshold for an event” particularly important ?

A

for the demonstration of meaningful results (rather than simply significant results)

26
Q

define “rejecting the null hypothesis” :

A

proposed effect is demonstrated when the null hypothesis is “rejected”

27
Q

if you “reject” the null hypothesis, what might you conclude ?

A

that the alternative hypothesis (your research hypothesis) is likely to be correct

28
Q

TRUE OR FALSE

“never accept the null hypothesis”

A

TRUE - complete unprovable certainty

29
Q

what is the decision matrix ?

A

evaluates and prioritizes a list of options and is a decision-making tool

30
Q

what does “reject the null” =

A

there is no change

31
Q

what is a type 1 error ?

A

false positive

32
Q

what is a type 2 error ?

A

a false negative (incorrect statement)

33
Q

define a type 1 error rejection type :

A

is an incorrect rejection of Ho

34
Q

define a type 2 error rejection type :

A

is an incorrect failure to reject Ho

35
Q

define a power rejection type :

A

a correct rejection of Ho

36
Q

TRUE OR FALSE

type 2 errors are used as power …. as ability to detect change

A

TRUE

37
Q

When the p-value is less than 0.05 then which of the following is accepted?

A

If the p-value is less than 0.05, it is judged as “significant,” and if the p-value is greater than 0.05, it is judged as “not significant.” However, since the significance probability is a value set by the researcher according to the circumstances of each study, it does not necessarily have to be 0.05.

38
Q

what is an upper-tailed test (direction hypothesis) ?

A

a t-statistic that is positive and far from zero would then lead us to favor the alternative hypothesis

39
Q

what is an lower-tailed test (direction hypothesis) ?

A

the p-value is the area under the curve of the t-distribution (with n−1 degrees of freedom) to the left of the observed t-statistic

40
Q

what is a non-directional hypothesis ?

A

does not predict the exact direction or nature of the relationship between the two variables

41
Q

what does statistical decision-making require ?

A

that you make a determination about the likelihood that something could occur due to chance

42
Q

why is it important to establish rejection regions ?

A

represents an area of low probability that the null hypothesis, H0 is true

43
Q

YES OR NO

is the p-value the actual probability (p = less than 0.05 “alpha”)

A

YES

44
Q

when our sample mean falls within the rejection region we …

A
  • reject the null hypothesis
  • conclude that the alternative hypothesis is likely to be correct
45
Q

FILL IN THE BLANK
“ the p value in null hypothesis significance testing is conditioned on the null hypothesis being ______ “

A

true

46
Q

what does “the p value in null hypothesis significance testing is conditioned 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

47
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 %

48
Q

what need to be explicit on how p values are used and defined ?

A

scientific journals and textbooks

49
Q

the use of what should become more widespread ?

A

bayesian statistics

50
Q

what is now widely recognized as providing quite limited information bout our data, and as being easily misinterpreted ?

A

p

51
Q

TRUE OR FALSE

you can augment you p-value with information about how confident you are in it, how likely it is that you will get a similar p-value in a replicate study, or the probability that a statistically significant finding is in fact a false positive

A

TRUE

52
Q

TRUE OR FALSE

you cannot enhance the information provided by frequentist statistics with a focus n effect sizes and a quantified confidence that those effects sizes are accurate

A

FALSE

you CAN enhance the information provided by frequentist statistics with a focus n effect sizes and a quantified confidence that those effects sizes are accurate

53
Q

TRUE OR FALSE

you cannot augment or substitute p-values with the Bayes factor to inform not he relative levels of evidence for the null hypotheses; this approach is particularly appropriate for studies where you wish to keep collecting data until clear evidence for or against your hypothesis has accrued

A

FALSE

you CAN augment or substitute p-values with the Bayes factor to inform not he relative levels of evidence for the null hypotheses; this approach is particularly appropriate for studies where you wish to keep collecting data until clear evidence for or against your hypothesis has accrued

54
Q

what can take the place of the p-value where you are using multiple variables to predict an outcome though model building, providing quantified information on what model is best ?

A

Akaike information criterial

55
Q

TRUE OR FALSE

the false positive rate associated with a p value of .05 is usually around 30%, but can be much higher

A

TRUE

Research Design Human Movement 2032 (11 decks)

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