Psyc 243 - Exam 3

Front 1

what does probability convey about an event's occurrence?

Back 1

it's our expectation or confidence in the event

Front 2

what is the probability of a random event based on?

Back 2

the relative frequency of the event in the population

Front 3

what is random sampling?

Back 3

selecting a sample so that every individual or score in the population has an equal chance of being selected

Front 4

what is sampling with replacement

Back 4

replacing the individuals or events from a sample break into the population before another sample is selected

Front 5

what is sampling without replacement?

Back 5

is not replacing the individuals or events from a sample before another is selected

Front 6

how does sampling without replacement affect the probability of events, compared to sampling with replacement?

Back 6

over successive samples, sampling without replacement increases the probability of an event, because there are fewer events that can occur; with replacement, each probability remains constant

Front 7

what are events independent?

Back 7

when the occurrence of one does not influence the probability of the other

Front 8

what are events dependent?

Back 8

when the occurrence of one influences the probability of the other

Front 9

what does the term "sampling error" indicate?

Back 9

it indicates that by chance, we've selected too many high or low scores so that our sample is unrepresentative. then the mean does not equal (mu) that it represents.

Front 10

when testing the representativeness of a sample mean, what is the criterion probability?

Back 10

it is the probability that defines means as too unlikely to accept as representing the (mu) of the underlying raw score population

Front 11

when testing the representativeness of a sample mean, what is the region of rejection?

Back 11

it is the area in the tail(s) of a sampling distribution containing the least likely and least representative sample means

Front 12

when testing the representativeness of a sample mean, what is the critical value?

Back 12

it is the minimum z-score needed for a sample to lie in the region of rejection

Front 13

what does comparing a sample's z-score to the critical value indicate?

Back 13

it indicates whether or not the sample's z-score ( and the samples (mean)) lies in the region of rejection

Front 14

what is the difference between using both tails verses one tail of the sampling distribution in terms of the size of the region of rejection?

Back 14

when using both tails, one-half of the region of rejection is in each tail of the sampling distribution

when using one tail, the entire region of rejection is in one tail of the distribution

Front 15

what is the difference between using both tails verses one tail of the sampling distribution in terms of the critical value?

Back 15

using both tails involves one critical value (usually ± 1.96);

using one tail involves a different critical value (usually ±1.645).

Front 16

why does random sampling produce representative samples?

Back 16

because by chance the sample has the same characteristics as the population

Front 17

why does random sampling produce unrepresentative samples?

Back 17

because by chance the sample does not have the same characteristics as the population

Front 18

why, if a sample mean is in the region of rejection,do we reject that the sample represents the underlying raw score population?

Back 18

because it is a mean that would virtually never occur if we had been representing that population

Front 19

why, if a sample mean is not in the region of rejection do we retain that sample represents the underlying raw score population?

Back 19

because we have a mean that occurs frequently and is likely when representing this population

Front 20

when we compute the z-score of a sample mean, what must you compute first?

Back 20

the standard error of the mean

Front 21

when we compute the z-score of a sample mean it describes the mean's location among other means on what distribution?

Back 21

the sampling distribution of means

Front 22

when we compute the z-score of sample means, what does the distribution show?

Back 22

it shows all possible means from a raw score population and their frequency

Front 23

Why does the possibility of sampling error present a problem to researchers when inferring a relationship in the population?

Back 23

sample may:

1) poorly represent one population because of sampling error or

2) represent some other population

Front 24

what are inferential statistics used for?

Back 24

they are used for deciding whether or not sample data are likely to represent a particular relationship in the population

Front 25

what does αlpha stand for and what two things does it determine?

Back 25

stands for the criterion probability

it determines the size of the region of rejection and the theoretical probability of a Type 1 error

Front 26

what are two major categories for inferential procedures?

Back 26

parametric and non parametric procedures

Front 27

what characteristics of your data determine which inferential procedure you must use?

Back 27

use parametric procedures with a normally distributed, interval, or ratio dependent variable; otherwise use, nonparametric procedure

Front 28

what happens if you seriously violate the assumptions of a procedure?

Back 28

the true probability of a Type 1 will be larger than our alpha

Front 29

what is a statistical reason to design a study so you can use parametric procedures?

Back 29

they are more powerful

Front 30

what are experimental hypotheses?

Back 30

they describe the predicted relationship that may or may not be demonstrated in an experiment

Front 31

what does H0 communicate?

Back 31

the null hypothesis indicates that the sample represents a population such that the predicted relationship does not exist

Front 32

what does Ha communicate?

Back 32

the alternative hypothesis indicates that the sample represents a population such that the predicted relationship does exist

Front 33

why do you use one-tailed test?

Back 33

when predicting the direction the scores will change

Front 34

when do you use a two-tailed test?

Back 34

when predicting a relationship but not the direction that scores will change

Front 35

what does "significant" convey about the results of an experiment?

Back 35

the sample relationship was so unlikely to occur if there was not a relationship in nature that we conclude there is a relationship in nature

Front 36

why is obtaining significant results a goal of behavioral research?

Back 36

because we want to study relationships and we believe that we've found only one when it is significant

Front 37

why is declaring the results significant not the final step of the study?

Back 37

because the goal is to understand the relationship and behaviors, so the final step is to understand and interpret the relationship.

Front 38

what is power?

Back 38

the probability of not making a Type II error

Front 39

why do researchers want to maximize power?

Back 39

so we can detect relationships when they exist and thus learn something about nature.

Front 40

what results makes us worry whether we have sufficient power?

Back 40

when results are not significant, we worry if we missed a real relationship

Front 41

why is a one-tailed test more powerful than a two-tailed?

Back 41

in a one-tailed test the critical value is smaller than in a two-tailed test; so the obtained value is more likely to be significant

Front 42

What are the advantage and disadvantage of two-tailed test?

Back 42

the advantage is that we need not predict the direction in which the IV will change the dependent score

the disadvantage is they are less powerful

Front 43

what are the advantage and disadvantage of one-tailed tests?

Back 43

the advantage is they are more powerful

the disadvantage is that we must accurately predict the direction in which the indepndent variable will change the dependent scores

Front 44

when the assumptions of a procedure require normally distributed interval/ratio scores, are we referring to scores on the independent or dependent variable?

Back 44

dependent variable

Front 45

what distinguishes an interval and ratio variable from nominal or ordinal variables?

Back 45

interval and ratio scores measure actual amounts, but an interval variable allows negative scores

nominal variables are categorical variables

ordinal scores are the equivalent of 1st, 2nd, etc.

Front 46

what distinguishes a skewed versus a normal distribution?

Back 46

a normal distribution is bell shaped and symmetrical with two tails

a skewed distribution is asymmetrical with only one pronounce tail

Front 47

what does a sampling distribution of means show?

Back 47

all means that occur by chance when sampling a particular raw score population

Front 48

a mean having a z beyond +/- 1.96 is where?

Back 48

in the region of rejection

Front 49

how often do means in the region of rejection occur when dealing with a particular raw score population?

what does this tell you about your mean?

Back 49

if (alpha) = 0.05, then less than 5% of the time

that it is less than a 5% chance of representing the raw score population

Front 50

what is the difference between a real relationship and one produce by sampling error?

Back 50

a real relationship occurs in nature (in the"population"). one produced by sampling error occurs by chance, and only in the sample data

Front 51

what does a relationship produced by sampling error tell us about nature?

Back 51

nothing

Front 52

why can no statistical result prove that changing the independent variable causes the dependent score to change?

Back 52

because statistics don't "prove" anything

it is always possible that some hidden variable is the cause

Front 53

what one thing does a significant result prove?

Back 53

that our sampling mean was unlikely to occur if we were representing a particular raw score population

Front 54

why are there different values of t(crit) when samples have different Ns?

Back 54

different Ns produce differently shaped t-distributions, so a different t(crit) is needed to demarcate a region of rejection equal to (alpha)

Front 55

what must you determine in order to find t(crit)?

Back 55

the degrees of freedom?

Front 56

what is the symbol for Pearson correlation coefficient in the population?

summarize the steps involved in analyzing a Pearson correlational study

Back 56

p

determine if the coefficient is significant by comparing it to the appropriate critical value;

if the coefficient is significant, compute the regression equation and graph it, compute the proportion of variance accounted for, and interpret the relationship

Front 57

summarize the steps involved in analyzing the results of a one-sample experiment

Back 57

create a hypothesis and design a study to obtain (mean) of dependent scores under one condition to compare to known (mu) under another condition ; determine if (mean) differs significantly from (mu) ; if it does, compute confidence interval for (mu) being represented, and interpret relationship psychologically

Front 58

what is the final step when results are significant in any study?

Back 58

to describe the relationship and interpret it psychologically, sociologically. etc.

Front 59

what is power?

Back 59

the probability of rejecting Ho when it is false (not making a Type II error)

Front 60

what outcome should cause you to worry about having sufficient power?

why?

Back 60

when a result is not significant

because then we may have made a Type II error

Front 61

at what stage do you build in power?

Back 61

when designing a study

Front 62

what are the three aspects of maximizing the power of a t-test?

Back 62

maximizing the difference between (mean) and (mu)

minimizing the variability in independent scores

maximizing N

Front 63

what are the three aspects of maximizing the power of a correlation coefficient?

Back 63

avoid a restricted range of X or Y

minimize the variability in the Y scores at each X scores

maximize N

Front 64

while conducting a one-sample experiment:

what two parametric procedures are available?

what is the defining factor for selecting between them?

what are other assumptions of the t-test?

Back 64

the t-test and z-test

compute z if the standard deviation of the raw score population is known; compute t if raw score population is estimated by Sx

that we have 1 random sample of interval or ratio dependent scores, and the scores are approximately normally distributed

Front 65

inferential statistics

Back 65

procedures for deciding whether sample data represents a particular relationship in the population

Front 66

parametric

Back 66

inferential procedures require assumptions about the raw score populations being represented

they are performed when we compute the mean

Front 67

nonparametric

Back 67

inferential procedures do not require stringent assumptions about the population being represented. they are performed when we compute the median or mode

Front 68

why must a relationship be significant to be important?

Back 68

we must first believe that it is a real relationship, which is what significant indicates

Front 69

why can a relationship be significant and still be unimportant?

Back 69

significant means only that we believe that the relationship exists in the population; a significant relationship is unimportant if it accounts for little of the variance

Front 70

whats the difference between the purpose of descriptive and inferential statistics?

Back 70

descriptive procedures are for summarizing important characteristics of data;

inferential procedures are for deciding whether the sampling relationship is likely to be found in the population (in nature)

Front 71

when should you use a parametric versus a nonparametric inferential procedure?

Back 71

use parametric procedures when the data are normally distributed, interval or ratio scores

use nonparametric with any other type of score

Front 72

what is the design of the study when we compute the z-test and t-test versus when we compute a correlation coefficient?

Back 72

the z- and t-test are used to compare the mu of a population when exposed to one condition of the independent variable to the scores in a sample that is exposed to a different condition. a correlation coefficient is used when we have measure the X and Y scores of a sample

Front 73

what does a correlation coefficient tell you?

Back 73

it indicates the strength of the linear relationship between a set of X and Y scores

Front 74

when do you compute r?

rho?

Back 74

pearson coefficient

with normally distributed, interval or ratio X and Y scores

for sample

rho = population

Front 75

when is linear regression used?

Back 75

with a significant Pearson r to predict Y (criterion) scores based on participants' X (predictor) scores

Front 76

what do we mean by the restriction of range?

why is it a problem for the size of correlation coefficient?

Back 76

when the range of X or Y is limited and artificially small

it produces an artificially small r

Front 77

why is it a problem for the power of a correlation coefficient to have restriction of range?

Back 77

a smaller r reduces power, increasing the likelihood that we will miss a relationship that exists in nature

Front 78

a scientist has conducted a two sample experiment:

a) what two parametric procedures are available?

b) what is the deciding factor for selecting between them?

Back 78

the independent-samples t-test and the related samples t-test

whether the scientist created independent samples or related samples

Front 79

how do you create independent samples?

Back 79

created when selecting and assigning participants to a sample without considering those selected for the other sample

Front 80

what are the two ways to create related samples?

what other assumptions must be met before using either two-sample t-tests?

Back 80

each score in one sample is paired with a score in the other sample by matching pairs of participants or by repeatedly measuring the same participants in all conditions

the scores are interval or ratio scores; the population are normal and have homogenous variance

Front 81

what is homogeneity of variance?

Back 81

occurs when the variances of the populations represented in our study are equal

Front 82

what is the difference between n and N?

Back 82

n is the number of scores in each condition

N is the number of scores in the experiment

Front 83

all other things being equal, should you create a related-samples or an independent-samples design? why?

Back 83

a reason to prefer a related design, if appropriate, is because the related samples t-test is more powerful

Front 84

what does the confidence interval for mu(d) indicate?

Back 84

it indicates a range of values of mu(d), any one of which (D) is likely to represent

Front 85

what does a confidence interval for the difference between two (mu)s indicate?

Back 85

it indicates a range of differences between two (mu)s, one of which is likely to be represented by the difference between our sample means

Front 86

what does effect size indicate?

what does d indicate?

Back 86

it indicates the size of the influence that the independent variable had one dependent scores

it measures effect size using the magnitude of the difference between the means of the conditions

Front 87

why is effect size useful during the final task after completing the analysis of significant results?

Back 87

because it indicates the size of the impact the independent variable has on the dependent variable and the behavior it reflects

Front 88

after obtaining a statistically significant two sample t(obt) what are three things you should do to complete your analysis?

Back 88

graph the results, compute the appropriate confidence interval, and compute the effect size

Front 89

what is the difference between an experiment vs a correlation study in terms of the design?

Back 89

we actively change one (independent) variable and measure the other to produce a relationship vs simply measuring two variables to observe a relationship that might be present

Front 90

what is the difference between an experiment vs a correlation study in terms of how we examine the relationship?

Back 90

we compute the mean in each condition to see if dependent scores change as the conditions change vs computing the correlation coefficient to summarize the entire relationship at once

Front 91

what is the difference between an experiment vs a correlation study in terms of how sampling error might play a role?

Back 91

sampling error might produce misleading (means) by chance vs producing the misleading correlation coefficient by chance

Front 92

what does it mean to "account for variance"?

Back 92

to predict Y scores by knowing someone's X score

Front 93

how do we predict score in an experiment?

Back 93

we predict the mean of a condition for participants in the condition

Front 94

which variable is an experiment is potentially the good predictor and important?

when does that occur?

Back 94

independent variable

when the predicted mean score is close to most participants' actual score

Front 95

in an experiment. what are three ways to try to maximize power?

Back 95

produce large differences between the means of the conditions; have small variability in scores within each condition; take a large n in each condition

Front 96

what does maximizing power do in terms of our errors?

Back 96

it minimizes the chances of making a Type II error

Front 97

for what outcome is it most important for us to have maximum power and why?

Back 97

if the results are not significant, so we can be confident that we did not miss a relationship that exists in nature

Front 98

you have performed a one-tailed t-test. when computing a confidence interval, should you use the one tailed or two tailed t(crit)?

Back 98

a confidence interval always uses the two-tailed t(crit)