WEEK #4 - research method Flashcards by Adriana Drakopoulos

TRUEP OR FALSE

if everything is equal we should expect a normal curve

TRUE

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TRUE OR FALSE

computers are better at random generated numbers

FALSE

computers are not good at random generated numbers

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what are standard scores ?

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

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what are standard deviations ?

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

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when are standard scores useful ?

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

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the following is an example of what ?

“

standard scores

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

to use standard scores

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what type of standard scores are most commonly used ?

z score or T score

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

T score

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what is the z-score ?

indicates how much a given value differs from the standard deviation

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what is the z-distribution ?

ranges from negative infinity to positive infinity
has a mean of 0
has a standard deviation of 1

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TRUE OR FALSE

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

FALSE

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

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what is the t score ?

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

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when do we use T-scores ?

to make numbers more valuable as their positive numbers

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what are percentiles ?

refers to the proportion scoring less than a particular value

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TRUE OR FALSE

we can also express z-scores as percentiles ?

TRUE

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where are percentiles obtained by ?

a z-table

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what do we obtain from a z-table ?

percentiles

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YES OR NO

can we sample a population ?

NO we can’t

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what do we look for during sample distribution ?

pretty close @ random sample
sample age
diversity
sex & gender

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what are “alternative hypotheses” ?

alternative explanation from the status quo

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what is “research hypotheses” ?

researchers true expectation of results

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what is a “null hypothesis” ?

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

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

nothing has happened; or
no relationship exists; or
no change has occurred

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

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

define "rejecting the null hypothesis" :

proposed effect is demonstrated when the null hypothesis is "rejected"

if you "reject" the null hypothesis, what might you conclude ?

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

TRUE OR FALSE "never accept the null hypothesis"

TRUE - complete unprovable certainty

what is the decision matrix ?

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

what does "reject the null" =

there is no change

what is a type 1 error ?

false positive

what is a type 2 error ?

a false negative (incorrect statement)

define a type 1 error rejection type :

is an incorrect rejection of Ho

define a type 2 error rejection type :

is an incorrect failure to reject Ho

define a power rejection type :

a correct rejection of Ho

TRUE OR FALSE type 2 errors are used as power .... as ability to detect change

TRUE

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

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.

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

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

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

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

what is a non-directional hypothesis ?

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

what does statistical decision-making require ?

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

why is it important to establish rejection regions ?

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

YES OR NO is the p-value the actual probability (p = less than 0.05 "alpha")

YES

when our sample mean falls within the rejection region we ...

- reject the null hypothesis - conclude that the alternative hypothesis is likely to be correct

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

true

what does "the p value in null hypothesis significance testing is conditioned on the null hypothesis being true" mean ?

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

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 __________ %

30-60 %

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

scientific journals and textbooks

the use of what should become more widespread ?

bayesian statistics

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

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

TRUE

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

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

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

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

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 ?

Akaike information criterial

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

TRUE