Define a within-participants design.

all participants experience all levels of the IV

What are the different ways we can classify variables?

Qualitative, Quantiative, Discrete, Continuous, Nominal, Ordinal

variable that systemically varies along w/ the IV so can't tell IV effects

What does a high p value tell us?

we can't rule out the null hypothesis as a reasonable explanation

Define a between-participants design.

participants randomly assigned to 1 & only 1 level of the IV

formal description of the process that generates data

What is a correlation?

measuring the relationship between 2 variables

Why does the drawer effect matter?

we don't know how many tests occured that weren't statistically significant, higher probability of Type I error

What is within-groups variance?

How spread out are the scores within each individual group?, measures how noisy the DV measurement is in this population

What are some of the shortcomings of null hypothesis significance testing?

a statistically significant result isn't necessarily a big/important result, small p value only tells us that results were unlikely due to the null, p values don't tell us anything about the research hypothesis, Large p values only tell us that we can't rule out the null hypothesis

What is the shape of the null distribution for an ANOVA and why is it shaped that way?

Positively skewed, F-ratios can't be 0

researcher noticing relationship in data, creating & presenting new theory, Hypothesizing After the Results are Known, new theory/hypothesis explains the relationship in the data

How do we measure variability in a data set?

Standard Deviation (SD), range, variance

alright y'all... the end of the semester is here and you made it! just a little heads up: this quiz is long. get ready

omg i'm so ready to be out of here, huh?!, how long is this?, yay!

What is the problem with running post-hoc tests?

as you compare individual groups each one has a chance to be a Type I error

What does it mean when we reject the null?

the null hypothesis doesn't provide a compelling origin story for our data

What are independent variables (IVs)?

the variable that the researcher manipulates and has levels attached to it

What does rejecting the null hypothesis for an ANOVA tell us?

the result was unlikely to have been due to the null hypothesis

measure of how many SDs certain score is above/below the distribution mean

What advantages does Bayesian analysis offer over NHST?

estimate probabilities of different models & see which ones are most likely, Bayesian analysis gives you a lot more info than testing a null model

What does the standard deviation tell us about a data set?

tells you about how far you expect any one value to be from the mean

How does linear regression differ from correlation?

must decide which one will be used to predict & which will be predicted

How can we fix those problems with post-hoc tests?

selecting a smaller alpha level so that overall alpha level is below 5%

What is a t statistic? How does it relate to statistical significance?

parametric test statistic used to look at the difference between two groups

Given a data set, what’s the right measure of central tendency to use? Why?

it depends on the shape of the distribution

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K300 Statistics Final Exam Review

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Answers

1
Define a within-participants design.
participants randomly assigned to 1 & only 1 level of the IV
participants are carefully assigned to 1 & only 1 level of the IV
all participants experience all levels of the IV
2
What are the different ways we can classify variables?
Qualitative, Quantiative
confusing
Discrete, Continuous, Nominal, Ordinal
Not helpful, stupid
3
Replication drastically increases our belief that the original results weren't due to a Type I Error
True
False
4
How can you gauge your level of confidence in the prediction made using a regression line?
r2
s2
t2
c2
5
Parametric statistics are different from the simulate-build-model approach because software can setup models for us
True
False
6
What are confounds?
the variable that the researcher manipulates and has levels attached to it
variable that systemically varies along w/ the IV so can't tell IV effects
variable expected to change as a result of IV manipulation & measured
7
What does a high p value tell us?
we can rule out the null hypothesis
we can't rule out the null hypothesis as a reasonable explanation
8
Effect size is measure of how far manipulation of the IV shifted the population measured in terms of the population’s SD
False
True
9
The correlational coefficient tells us the direction & strength of the correlation
True
False
10
An absolute statsitic is when you want to make a bigger value look small
False
True
11
Define a between-participants design.
participants randomly assigned to 1 & only 1 level of the IV
all participants experience all levels of the IV
participants are carefully assigned to 1 & only 1 level of the IV
12
you interpret and “calculate” the probability notations used in Bayesian analysis via: P(H|D) = P(H) * P(D|H)/P(D)
True
False
13
A posterior is probability of an event before new data is collected, a prior is probability of an event after new data
False
True
14
P-hacking is tweaking your analysis before you collect data in order to make your results stastically significant
True
False
15
What is a model?
formal description of the process that generates data
hypothesis that says that differences in your data are there b/c of noise
hypothesis that tells us that our research hypothesis is right
16
What is a correlation?
manipulating 1 variable & measuring the other
measuring the relationship between 2 variables
17
Sample size matters because data from large sample is likely to deviate away from the expected result
True
False
18
A relative statistic is when you want to make a small change look big
True
False
19
The null hypothesis says that the differences in our data are there because of noise, dumb luck, random chance, etc
False
True
20
Z scores differ from a “raw” score because they're sometimes called "standardized scores"
True
False
21
Why does the drawer effect matter?
we know how many tests were done without being statistically significant
we don't know how many tests occured that weren't statistically significant
higher probability of Type I error
22
What is within-groups variance?
measures how much IV manipulation affected the DV
How spread out are the mean values of each group or condition?
How spread out are the scores within each individual group?
measures how noisy the DV measurement is in this population
23
We measure "effect size" for a correlation via the correlation constant
False
True
24
The "best fitting" line can be predicted by minimizing the distance b/w y values of predicted line & data's y values
True
False
25
What are some of the shortcomings of null hypothesis significance testing?
a statistically significant result isn't necessarily a big/important result
small p value only tells us that results were unlikely due to the null
p values don't tell us anything about the research hypothesis
Large p values only tell us that we can't rule out the null hypothesis
26
What is the shape of the null distribution for an ANOVA and why is it shaped that way?
Positively skewed, F-ratios can't be 0
Symmetric, F-ratios can go under & above 0
Negatively skewed, F-ratios can't be over 1
27
What is HARKing?
Subsequent pairwise comparisons to see exactly where the difference lies
researcher noticing relationship in data, creating & presenting new theory
Hypothesizing After the Results are Known
new theory/hypothesis explains the relationship in the data
28
Replication is method of combining results from multiple related studies
True
False
29
How do we measure variability in a data set?
Standard Deviation (SD), range, variance
z scores
30
alright y'all... the end of the semester is here and you made it! just a little heads up: this quiz is long. get ready
omg i'm so ready to be out of here
huh?!
how long is this?
yay!
31
What is the problem with running post-hoc tests?
as you compare individual groups, they all start to look the same
as you compare individual groups, they all start making no sense
as you compare individual groups each one has a chance to be a Type I error
32
What does it mean when we reject the null?
the null is absolutely right
we can't rule out the null as a reasonable explanation for our data
the null hypothesis doesn't provide a compelling origin story for our data
the null is completely wrong
33
Bayesian analysis is different from NHST because we update our beliefs after collecting some new data is observed
False
True
34
What are independent variables (IVs)?
variable that systemically varies along w/ the IV so can't tell IV effects
variable expected to change as a result of IV manipulation & measured
the variable that the researcher manipulates and has levels attached to it
35
A formal results statement for t tests/ANOVAs is: There was/was not a significant effect on [x & y], t/F(N-1), p = #
True
False
36
What does rejecting the null hypothesis for an ANOVA tell us?
the result was unlikely to have been due to experimental manipulation
the result was likely to have been due to the null hypothesis
the result was unlikely to have been due to the null hypothesis
37
What is a Z score?
measure of how far, on average, each score is from the mean
how much errorwe expect when estimating pop mean w/ N size sample mean
measure of how many SDs certain score is above/below the distribution mean
38
Rejecting the null hypothesis doesn't tell us anything about how strongly the manipulation of the IV affected the DV
True
False
39
What advantages does Bayesian analysis offer over NHST?
Bayesian analysis gives you a lot less info than testing a null model
estimate probabilities of different models & see which ones are most likely
Bayesian analysis gives you a lot more info than testing a null model
40
The equation for population variance is different from the equation for a sample's variance because of the "N-1"
True
False
41
Meta-analyses make it easier to estimate effect sizes & allow the results to be generalized to a wider population
True
False
42
What does the standard deviation tell us about a data set?
how many SDs a certain score is above/below the distribution mean
how much error we expect when estimating population mean w/ size N mean
tells you about how far you expect any one value to be from the mean
43
What are histograms?
frequency distributions
when researchers make a hypothesis after data is collected
ANalysis Of VAriance
44
How does linear regression differ from correlation?
z will always play a factor in the correlation
must decide which one will be used to predict & which will be predicted
it doesn't matter which one we call x & y
45
We move from testing 2 groups to testing more than 2 groups by using variance measures to analyze more than 2 groups
False
True
46
Meta-analysis- running another experiment to see if replication results are extreme enough to reject the null hypothesis
True
False
47
Correlational studies differ from experimental studies because they simply measure the variables & their relationship
False
True
48
How can we fix those problems with post-hoc tests?
selecting a bigger alpha level so that overall alpha level is above 5%
selecting a smaller alpha level so that overall alpha level is below 5%
selecting a medium alpha level so that overall alpha level stays the same
49
Z scores are useful because this standardization doesn't allow you to easily compare scores from different distributions
True
False
50
What is a t statistic? How does it relate to statistical significance?
parametric test statistic used to look at the difference between two groups
tweak results after collecting data to have a statically significant result
51
Given a data set, what’s the right measure of central tendency to use? Why?
always use the mean
always use the mode
always use the median
it depends on the shape of the distribution
52
What if the data we have is inconsistent (very unlikely) with the model?
model is right and tells us that our hypothesis is correct
model is very unlikely to be the origin story for our data
model could be an origin story for our data
53
Why do they matter when interpreting statistical claims?
force us to fail to reject the null
allow us to better understand the likelihood of certain events occurring
allow us to reject the null
54
In linear regression, which variable is the predictor and which is the criterion variable?
predictor is x & criterion is y
predictor is y & criterion is x
55
What can correlational analysis tell us?
if there's a relationship between 2 (or more) variables
if one variable caused another to occur
56
What does the histogram of results from 10,000 runs of the simulation represent?
stimulation of wrong answers made by IU admins
distribution of results that the model predicts after 10,000 runs if [z]
the # of [x] out of [y] that got it right if the model is true
57
Why are models useful?
allow us to estimate the variance of a data set
allow us to estimate the p value
b/c they stimulate complex process & create distribution of likely outcomes
58
What are “base rates”?
probability of getting a result at least as extreme as the experimental one
# of times we'd expect some outcome to occur in non-intervention population
59
What is an alpha level?
probability of making an error when the null hypothesis is wrong
probability of making an error when the null hypothesis is true
60
What are dependent variables?
variable expected to change as a result of IV manipulation & measured
the variable that the researcher manipulates and has levels attached to it
variable that systemically varies along w/ the IV so can't tell IV effects
61
Define a paired-participants design.
all participants experience all levels of the IV
participants randomly assigned to 1 & only 1 level of the IV
participants are carefully assigned to 1 & only 1 level of the IV
62
A Type I error is when the null is incorrect but we concluded that we should fail to reject the null
True
False
63
What do effect sizes tell us?
It tells us how far the population would be shifted
It tells you how far the mean would be shited
It tells you how far the null is off
64
How do you describe the shape of a histogram?
flat
symmetric, skewed, unimodal, bimodal, multimodal
65
A post-hoc test is subsequent pairwise comparisons to determine exactly where the difference lies
True
False
66
What are measures of central tendency?
range
Mean, Median, Mode
variance
standard deviation
67
What can we conclude about a model if the data we have is consistent with the model?
model could be an origin story for our data
model is wrong and doesn't tell us anything about our hypothesis
model is very unlikely to be the origin story for our data
68
Given a regression constant and coefficient, how can you predict the criterion variable given a predictor?
with an equation: SE = σ / √ (n)
with an equation: ŷ = a + bx -> given a specific x value, we can predict ŷ
with an equation: y = mx + b
69
The goal of linear regression is to use a line to make an exact prediction
False
True
70
How do you calculate a Z score for a single value?
with a modal
with an ANOVA
with a formula: z = (x-μ)/σ
with a graph
71
What does a low p value tell us?
we can't rule out the null hypothesis as a reasonable explanation
the null is right
the null is wrong and stinky
we can rule out the null hypothesis as a reasonable explationaion
72
What is the standard error?
how much error we expect when estimating pop mean w/ size N sample mean
how many SDs a certain score is above/below the distribution mean
distance between the highest & lowest points
73
What is an ANOVA and how does it differ from a t test?
ANalysis of Mean, uses SD
ANalysis Of RAnge, uses S statistic
ANalysis Of VAriance, uses F statistic (aka F-ratio)
74
What does one run of a simulation represent?
distribution of results that the model predicts after 10,000 runs if [z]
the # of [x] out of [y] that got it right if the model is true
75
How do we estimate the population variance?
with an ANOVA
with a model
We estimate the population variance via a formula: SD2 = ∑(X-M)2/N-1
76
How can you construct them?
y-axis: a measurement of how often data values popped up in the data set
i have no idea man
x-axis lists each of the possible data values
77
What is the file drawer effect?
Only publishing statistically significant results
publising results that have nothing to do with your original hypothesis
only publishing not statistically significant results
78
A con of correlational studies is that correlation doesn't imply causation
True
False
79
you made to the end! if no one else has told you this: I'm proud of you & everything you've done this semester <3
this final is gonna ruin me istg
omgggg
now onto the final!
<3 <3 <3
80
HARKing is forcing researchers to make experimental decisions BEFORE they collect their data
True
False
81
What are confidence intervals?
estimate of the mean of reasonable values of the parameter being reported
estimate of the median of reasonable values of the parameter being reported
estimate of the mode of reasonable values of the parameter being reported
estimate of the range of reasonable values of the parameter being reported
82
The 3 causal relationships are: A causes B, B causes A, or C has some effect
False
True
83
What are the sources of variability in the data from an experiment?
Variability due to Individual/Random Differences
Variability due to political affiliation
Variability due to Experimental Manipulation
84
What are the null/research hypotheses for hypothesis testing in linear regression?
null: knowing the x value doesn't help predict the y value
knowing the y value doesn't help predict the x value
null: knowing the x value helps predict the y value
research: knowing the x value helps predict the y value
85
What is between-groups variance?
measures how much IV manipulation affected the DV
How spread out are the scores within each individual group?
How spread out are the mean values of each group or condition?
measures how noisy the DV measurement is in this population
86
Define pre-registration.
stating what the reasearchers will do before the experiment starts
researchers clearly defining the different parts of their experiment
Subsequent pairwise comparisons to see exactly where the difference lies
87
also some questions aren't listed here but those questions are posted on the quizlet! alright let's go!
thanks for the heads up!
ok
alr cool
let's get started
88
How do we model the null hypothesis for correlations?
use the coin-flipping applet to model the distribution of results
use the matching applet to model the distribution of results
use parametric stats to generate results & be able to interpret the results
use an ANOVA to generate results & be able to interpret the results
89
A Type II error is when the null is correct but we concluded that we should reject the null
False
True
90
Pre-registration helps with better & more thoughtful study designs and reduces the temptations of p-hacking and HARKing
False
True
91
We evaluate a model once we have data by creating a simulation of the model
False
True
92
A p value is the probability that we'd get a result at least as extreme as our experimental result if the null were true
False
True
93
Alpha levels are important because they're used to judge if a test statistic is statisically significant
False
True

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