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Research Methods > exam 4 > Flashcards

exam 4 Flashcards

1
Q

inferential statistics

A

used to determine whether results match what would happen if we were to construct experiment again and again

(basically testing to see if sample means reflect a true difference in population means)

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2
Q

formal term for null hypothesis

A

Ho

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3
Q

goal of inferential statistics

A

determine chance (probability) of getting a particular outcome (sample) if our assumption about the world (null hypothesis) is true.

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4
Q

what do you do if probability is too low

A

reject null hypothesis

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5
Q

how to calculate probability

A

p=frequency/all outcomes-

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6
Q

null hypothesis

A

population means are equal-observed differences is due to random error

“there is no change, difference or relationship in the general population”

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7
Q

research hypothesis

A

population means are not equal

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8
Q

binomial distribution

A

likelihood that a value will take one of two independent values under a given set of parameters or assumptions. The underlying assumptions of the binomial distribution are that there is only one outcome for each trial, that each trial has the same probability of success, and that each trial is mutually exclusive, or independent of each other.

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9
Q

in a coinflip test, the binomial distribution represents the..

A

null hypothesis

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10
Q

possible outcomes=

A

population

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11
Q

probability of getting a particular outcome

A

sample

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12
Q

if the probability of your “sample” is high

enough given your null hypothesis…(“population”),

A

“fail to reject the null hypothesis”

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13
Q

if sample is too unlikely you..

A

“reject null hypothesis”

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14
Q

why do we say fail to reject null

A

Hypotheses can be rejected with certainty,

but the correct single one may never be identified…

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15
Q

research hypothesis in formal terms

A

H1

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16
Q

statistical significance

A

a significant result when the outcome has a very low probability of occurring if the population means were equal

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17
Q

what is the probability required for significance called

A

alpha level

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18
Q

alpha level

A

the probability required for significance

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19
Q

research hypothesis is also called..

A

alternative hypothesis

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20
Q

how to specify null hypothesis formally

A

u1=u2

u1=0.5

u1=u2=u3=u4..(iv has more than two levels)

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21
Q

how to specify if researcy hypothesis is correct in formal terms

A

u1 =/= u2

u1=/=0.5

u1>0.5, etc

“at least one differs from the rest’

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22
Q

sampling distributions are based on..

A

the assumption that the null hypothesis is true

deals with probability charts?

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23
Q

p value

A

actual probability of result if null hypothesis was true

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24
Q

importance of sampling size

A

as sample size increases, you’re more likely to obtain an accurate estimate of the true population value

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25
Q

Type I error

A

rejecting the null hypothesis (H0) when it is actually true

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26
Q

Type II error

A

failing to reject (or, accepting)
H0 when an alternative hypothesis (H1) is
actually true.

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27
Q

t test

A

used to examine weather two groups are statistically different to each other.

reflects all possible outcomes we could expect if we compare the means of two groups and null hypothesis true

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28
Q

what is the t value

A

ratio of two aspects of data, the difference between means and the variability within groups

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29
Q

group difference

A

the variance between obtained means.

(deals with t test)

under null hypothesis, you expect this to be 0

30
Q

the value of t increases as…

A

the difference between your obtained sample means increases

31
Q

degrees of freedom

A

(df) its the total number of participants minus the number of groups

32
Q

what determines if something is a one or two tailed test?

A

depends on whether you origionally designed your study to test a directional hypothesis

33
Q

what do you do if you cant calculate the probability of an outcome (step 2 of inferential statistics)

A

convert value to a statistic (eg a score/measure/etc) then determine the probability of getting that statistic

(USE Z SCORE! Z SCORE IS THE STATISTIC)

34
Q

statistic

A

a score/measure/etc

35
Q

statistical vs practical significance

A

statistical significance- whether an effect exist

practical-refers to the magnitude of the effect

36
Q

another name for significance level

A

alpha level

37
Q

probability of making a type one error is determined by..

A

determined by choice of significance or alpha level

38
Q

probability of type 2 error is determined by..

A

alpha level
sample size
effect size

39
Q

sampling distribution

A

other possible statistics of the same size

40
Q

The Central Limit Theorem

A

“When variables are added (such as w/ means), the distribution of these new values (e.g., 𝑋തs) will
approach a normal distribution even if the original variables themselves are not normally distributed”

41
Q

the central limit theorem suggest that..

A

with a large enough sample size, almost every sampling distribution becomes normal

42
Q

standard error

A

average distance between a sample mean and a population mean

standard deviation of the distribution of sample mean and pop mean

43
Q

factorial design

A

an experiment with more than one independent variable (also called factor)

44
Q

main effects

A

the effect one IV has by itself on the DV in a factorial design

45
Q

how do you know when theres interaction

A

when effect of one IV on the DV depends on the particular level of the other IV

46
Q

format for factorial designs

A

levels in first IV X #levels in 2nd IV Xnumber of levels in 3rd IV

47
Q

The ____ the t (or z) ratio, the more likely the results are ___

A

Larger

Significant

48
Q

R squared value

A

Represents the percentage of variability in u that can be accounted for in x

49
Q

A criterion variable is generally analogy’s to an ___variable in the experimental method

A

Dependent

50
Q

Precictor variable

A

Variable used in regression to predict another variable

Predicts outcome of critertion. Can also be thought of as dependent variable

51
Q

John finds Pearson r coefficient between variable a and b to be 0. Before concluding there is no relationship, what should he do?

A

Construct a scatter plot of the data

52
Q

T test and Pearson correlation are used to test what kind of data?

A

Interval

53
Q

Multiple correlation is a technique for

A

Increasing accuracy of prediction

54
Q

Ways to commit a Type two error

A

Alpha is too low

Sample size is too small small effect/small effect size

Experimenter error such as bad instructions or weak manipulation

55
Q

Main effect

A

The effect that one independent variable has by itself on the dependent variable

56
Q

The mean difference between levels of one factor are called the____

A

Main effects of that factor

57
Q

What do you plot to determine if there’s a main effect between interactions

A

Cell means

58
Q

What would a plotted data point look like if there’s a main effect

A

The data point won’t overlap

Overall differences in bar heights

59
Q

How would you determine if there’s no main effect

A

If plots overlap

60
Q

Interaction

A

When effect of one independent variable on the dependent variable depend on the particular levels of the other independent variables

61
Q

How to determine if there’s NO interaction between factors

A

Parallel lines or no changes in bar patterns

62
Q

R squared

A

Amount of variance explained

Percent of shared variance between two variables

63
Q

What is y prime

A

The predicted value of y (critertion variable)

64
Q

Y

A

The critertion variable

65
Q

X

A

Predictor variable

66
Q

A

A

The y value at the point where the regression line crosses the y axis

The value of when x=0

67
Q

B

A

Slope of the line

Related to but not the same as r

68
Q

Restricted range

A

Range of values that has been condensed or shortened

It makes the correlation misleading

69
Q

A correlation coefficient is always used to

A

Describe the strength and relationship between two variables

70
Q

Multiple correlation uses many ______ variables and one ___\\

A

Many predictor

One criterion

71
Q

Effect size

A

quantitative measure of the magnitude of the experimenter effect.

Research Methods (17 decks)

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