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Dats great > Summa Week 3 > Flashcards

Summa Week 3 Flashcards

1
Q

True or false: One purpose for central tendency is to find a single score for an entire distribution.

A

True (mean, median or mode describes the entire dataset)

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2
Q

True or false: If a sample of at least 30 scores is randomly selected from a normal population, the sample mean will be equal to the population mean.

A

False, the sampling distribution of the mean will be equal to the population mean (needs to be done a lot more to make that happen)

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3
Q

True or false: An obtained p-value of .001 means that, if you decide to reject the null hypothesis, the probability that you are making the wrong decision.

A

False - it assumes you know the exact amount of chances that it is true, rather than the probability of the null hypothesis being true - you can’t possibly no for sure what the chances are

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4
Q 
Grade  Male  Female  Total
A  18 12 30
B  30  30  60
C  53  27  80
D  12  8  20 
F  7  3  10
Total  120  80  200
The probability of a student with grade F is 10/200.
A

True.

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5
Q 
Grade  Male  Female  Total
A  18 12 30
B  30  30  60
C  53  27  80
D  12  8  20 
F  7  3  10
Total  120  80  200

The probability of a female student is 80/200.

A

True

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6
Q 
Grade  Male  Female  Total
A  18 12 30
B  30  30  60
C  53  27  80
D  12  8  20 
F  7  3  10
Total  120  80  200

The probability of a female B student is 30/200.

A

True

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7
Q 
Grade  Male  Female  Total
A  18 12 30
B  30  30  60
C  53  27  80
D  12  8  20 
F  7  3  10
Total  120  80  200
The probability of a male with grade below C is (12+7)/200.
A

True

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8
Q

When two fair dice are rolled, the probability of an even number or a 3 on the first die is (3/6)*(1/6).

A

False, this would use the addition rule (or adds, add multiplies)

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9
Q

When two fair d

A

False. And multiplies

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10
Q

For the population of scores shown in the frequency distribution table, the mean is:

FREQ DIST OF SCORES
SCORES   f
5     2
4     1
3     4
2     3
1     2

a) 15/5 = 3
b) 15/12 = 1.25
c) 34/5 = 6.80
d) 34/12 = 2.83

A

D

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11
Q

When two fair dice are rolled, the probability of an odd number on the first die and a 2 on the second die is

a) 1/6
b) 1/3
c) 1/2
d) 2/3
3) 1/12

A

e, 1/12

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12
Q

Age of children at Romper Room Daycare (sample of 5 children):

Name        Age
Mary           4.00
Rodriguez  3.00
Jacques     1.00
P.J.              5.00
Brigitte       2.00

What is the mean of the sample of children at the Romper Room Daycare?

a) 1.50
b) 2.00
c) 2.50
d) 3.00
e) 4.00

A

d)3

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13
Q

Age of children at Romper Room Daycare (sample of 5 children):

Name        Age
Mary           4.00
Rodriguez  3.00
Jacques     1.00
P.J.              5.00
Brigitte       2.00

What is the standard deviation of the sample of children at the Romper Room Daycare (a better estimate of the corresponding population standard deviation)?

a) 1.14
b) 1.37
c) 1.99
d) 1.58
e) 2.50

A

d) 1.58

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14
Q

Rebecca got an 80 on her calculus test. Her class mean was 70.2 with a standard deviation of 4.0. Jesse goes to a different school and got a 73 on his calculus test. His class mean was 50.0 with a standard deviation of 9.6. Who did better?

a) Rebecca
b) Jesse
c) Both did equally well
d) Both did equally poor
e) More information is needed to calculate.

A

Rebecca

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15
Q

Statistical inference involves statements about…?

A

probability

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16
Q

the probability theory letus us deduce propositions about the likelihood of various outcomes, if certain conditions are…true or false?

A

true

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17
Q

The probability of event A is denoted by p(A) = ?

A

the sum of p (all elementary outcomes of event A), or

0 <= p >= 1

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18
Q

What are the three approaches to probability theory?

A
  1. classical/analytic approach
  2. Frequentist approach
  3. subjective approach
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19
Q

What is an experiment?

A

an experiment or trial or scenario is any manipulated procedure that can be infinitely repeated and has a well-defined set of possible outcomes, known as the sample space

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20
Q

What is another term for experiment in probability theory?

A

trial

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21
Q

What is a well-defined set of possible outcomes in probability theory?

A

the sample space

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22
Q

What is an event in probability theory?

A

a set of outcomes of an experiment (a subset of the sample space) to which a probability is assigned; usually a datapoint

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23
Q

What is an elementary outcome in probability theory?

A

(also called an atomic event or simple event) is an event which contains only a single outcome in the sample space (one observation in a series of trials)

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24
Q

What is another name for an elementary outcome in probability theory?

A

an atomic event or simple event or an elementary event, which contains only a single outcome in the sample space

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25
Q

What are equally probable events?

A

what it says. E.g. rolling a die, or flipping a coin

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26
Q

What is simple random sampling?

A

each item has the same potential of being selected. THIS IS ASSUMED FOR MOST PSYCHOLOGICAL EXPERIMENTS

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27
Q

What is stratified random sampling?

A

more related to sociology by representing those that already exist in a sample e.g. gender or age within a sample which then may affect results

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28
Q

What is sampling without replacement?

A

like picking Tarot cards for the different cards for the Celtic cross. Not one card can be repeated, therefore increasing the chances of other cards being picked

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29
Q

Sampling with replacement?

A

picking a card from a deck and then replacing it immediately

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30
Q

What is the classical approach to probability theory?

A

assumes all conditions are fair and accurate, therefore equally likely and independent
e.g. 1/N probability of each outcome, or 1/6 for a die, or 1/52 for an ace of hearts card…

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31
Q

What is the easiest way to do probability on paper?

A

the tree diagram

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32
Q

What does mutually exclusivity in probability theory mean? Why would this be preferred in psychology?

i.e. p(A or B)

A

they cannot occur at the same time
e.g. all 3s cards and all hearts cards –technically you have to delete the 3 of hearts because it straddles both categories
p(A OR B) = p(A) + p(B) - p(A and B)
e.g. a heart and a 3, therefore
13 hearts/52cards + 4 3s/52 cards - 3 of hearts (1)/52 = 16 cards
MUTUALLY EXCLUSIVE RELATIONSHIP ADDS TOGETHER AND SUBTRACTS OVERLAP

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33
Q

For a multiple outcome relationship, what is the formula?

A

p(A and B) = p(A) x p(B)

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34
Q

What is the probability of getting at least one correct answer for three true and false questions (assuming equal chances that the answer to a problem is correct or wrong)?

A

mutually exclusive – true OR false

p(at least one correct answer) = 7/8

FFT
FTF
FFT
FFF - only instance we don't have a T (1/8 of getting no Ts, therefore answer is 7/8
TFT
TFF
TTF
TTT
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35
Q

What is the multiplication law of probability?

A

P (A and B) = P (A)
e.g. chance of being overweight and hypertensive over total hypertension rate equals likelihood of being overweight
= .10/.20

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36
Q

What is the frequentist approach in probability theory?

A

how often does all classical aproaches happen? it is a series of trials that nears closer to theoretical probability, or the LIMIT

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37
Q

What is the subjective approach?

A

based on your understanding,, what seems most likely to occur?

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38
Q

What is the key to understanding the three approaches to probability theory?

A

all descriptions should really reach the same conclusion despite the use of different methods

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39
Q

What is a posteriori?

A

p(A) + # of times A occurred / total # of events

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40
Q

What is a priori?

A

of events of A over total # of events

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41
Q

What is Baynes’ approach?

A

integrating prior knowledge into calculations of probability, kinda like a lessons learned theory

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42
Q

What is theory i’m paraphrasing?

“assumption B happened defines what the probability of A happens”

A

Baynes’ approach looks at the probability of the hypothesis being true

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43
Q

Baynes’ approach looks at how the probabilty of the hypothesis is…?

A

true

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44
Q

Psychology looks at the probability the hypothesis is…?

A

false / null hypothesis

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45
Q

What is another name for a normal distribution?

A

a Gaussian distribution

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46
Q

What are Z-scores?

A

descriptions of a population’s standard distributions

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47
Q

What is the standardized mean and SD for a Z-score?

A 
mean = 0
SD = 1
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48
Q

What is the formula for the Z-score?

A

(x - u/miu)

or

x = Z * miu + u

Is the population close to what is considered a normal, standardized population?

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49
Q

T or F: a standardized distribution will change the skew or kurtosis.

A

Naw!

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50
Q

What do we assume with a normal distribution?

A

we assume that our sample is normal, and based on a normal population

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51
Q

another name for skew

A

symmetry

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52
Q

another name for “flatness”

A

kurtosis

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53
Q

modality = mode, therefore uni-modality and bi-modality and multi-mode are…

A

1 mode, and 2-mode and multi-mode, respectively

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54
Q

What does a negative skew look like?

A

_____////////

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55
Q

What does a positive skew look like?

A

````______

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56
Q

what is platykurtic?

A

flatter in the middle, and less high overall

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57
Q

what is leptokurtic?

A

higher and more pointed in the middle, and more high overall

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58
Q

What graphic tool is useful for determining symmetry and/or kurtosis?

A

the Q-Q plot

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59
Q

How does the Q-Q plot represent data?

A

It shows how data can be skewed based on whether individual observations are close to the general trend line, or far from it

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60
Q

How is normality shown in a Q-Q plot?

A

observations are near the line that depicts the general trend of a graph

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61
Q

What are the moments of a distribution?

A

The tasks in order of a distribution

  1. mean
  2. variance
  3. skew
  4. kurtosis
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62
Q

What “moments” are given in SPSS

A

all of them -

standard error for M, skewness, and kurtosis (standard error is the type of variance)

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63
Q

What is the formula for Z-score?

A

statistic/SE.

if > 1.96, then significant at .05

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64
Q

What happens if the skew and/or kurtosis tests result in >1.96?

A

the data does not appear normal, and we can likely not continue to test for significance (t-test)

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65
Q

Let’s say Bill has an IQ of 145 and is 52 inches tall
IQ in the population has a mean of 100 and a standard
deviation of 15
Height in the population has a mean of 64” with a standard
deviation of 4

 How many standard deviations is Bill away from the average
IQ?

A

= statistic/SE
= 145-100/15
= 3, which is greater than 1.96,

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66
Q

Let’s say Bill has an IQ of 145 and is 52 inches tall
 IQ in the population has a mean of 100 and a standard
deviation of 15
 Height in the population has a mean of 64” with a standard
deviation of 4

 What is the Z score of the Bill’s height? Or what is Bill’s
height in Z scores?

A

= 52-64/4

= .20, less than 1.96

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67
Q

Consider two sections of statistics
 Gurnsey’s class has a mean of 80 and S of 5; Marcantoni’s
class has a mean of 70 and S of 5
 Student 1 gets 80 in Gurnsey’s class; student 2 gets 75 in
Marcantoni’s class
 Which student did better?

A

1: 0
2: 75-70/5 = 1
student 2 did better in his class

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68
Q

A _______________ test is used when s and m are known

and the sample is 30 or larger.

A

z-test

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69
Q

Which of the following is an assumption of the z test?
a. The data should be ordinal or nominal.
b. The population distribution of scores should be normal.
c. The population mean (m) is known, but not the standard
deviation (s).
d. The sample size is typically less than 30.

A

b. The population distribution of scores should be normal

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70
Q

Can disjoint events be independent?

A

Hell naw

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71
Q

If A and B cannot occur together (are disjoint), then knowing that a occurs does change probability that B occurs. T or F?

A

true

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72
Q

What is the probability rule for AND?

e.g. what are the chances that I will be married and successful?

A

p(A and B) = p(A) * p(B)

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73
Q

What is the probability of obtaining “three heads” with a four-coin toss?

p(three heads) =

A

p(A and B) = p(A) * p(B)

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74
Q

Are permutations or combinations dependent on the order in which they occur?

A

permutations!!

You can have any combo, but only one permanent permutation

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75
Q

What is the joint probability formula?

A

p(A) = Na/N
p(B) = Nb/N…
Therefore,
p(A and B) = Nab/N

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76
Q

What is the conditional probability formula?

A

the probability of one outcome is dependent on the occurrence of the other outcome
e.g. the probability of drawing two heart cards from a deck

p(A and B) = p(A) * p(B/A)
= 13/52 * 12/52

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77
Q

What is conditional probability?

e.g. p (has hypertension given that overweight, or owing to the fact that he or she is overweight)

A

the probability of an event A must often be modified after information is obtained as to whether or not a related event B has taken place
= p(A/B)
= .1 (overweight & hypertensive)/.25 (all overweight participants) = .4

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78
Q

Let A denote “overweight”, B denote “has hypertension”, p(overweight given that having hypertension)
i.e. p(A/B) = ?

A

= p(A/B)

= .1 (overweight & hypertensive)/.2 (all hypertensive participants = .5

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79
Q

When you think of the frequentist approach, I think of…

A

each time you do a trial, you frequent the theoretical limit, or desired event or proportion (more often closer to 1/2 when tossing a fair dime)

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80
Q

_______________ is the study of likelihood and uncertainty

A

probability

81
Q

The rule that says that the probability of a series of
outcomes occurring on successive trials is the product of
their individual probabilities is the _______________ rule

A

multiplication rule/”AND”

82
Q

The rule that says that the probability of one outcome or
the other outcome occurring on a particular trial is the sum
of their individual probabilities is the _______________ rule

A

OR

83
Q

The and rule is to _______________ and the or rule is to
_______________.
a. multiplication rule; addition rule
b. addition rule; multiplication rule
c. multiplication rule; multiplication rule
d. addition rule; addition rule

A

b. multiplication rule; addition rule

84
Q 
The probability of rolling either a 2 or a 5 on one roll of a standard die
is
a. .34
b. .25
c. .50
d. .03
A

a. .34 (2 options out of 6 possible ones)

1/6+1/6 = 2/6

85
Q

The probability of rolling a 2 followed by a 6 on a standard die is:

a. .34
b. .25
c. .50
d. .03

A

1/6 x 1/6 = 1/36 = .03
d. .03

IT IS MUCH MORE DIFFICULT TO DO SOMETHING AND THEN SOMETHING ELSE, THEREFORE THE AND RULE IS MULTIPLICATION

86
Q

What is considered exhaustive in probability?

A

A set of events are said to be exhaustive if it includes all possible outcomes

87
Q

Which of the following is true for a symmetrical distribution?

a) the mean, median, and mode are all equal
b) mean = median
c) mean = mode
d) median = mode

A

trick question…they all could technically work

88
Q

When we take a single sample mean as an estimate of the value of a population mean, we have:

a) a point estimate
b) an interval estimate
c) a population estimate
d) a parameter

A

a) a point estimate (one sample has one point to estimate where it is)

89
Q

When small samples are selected from a normal population

a) the value of sample mean tends to be too large
b) the sampling distribution of means follows Student’s t distribution
c) the value of s tends to be too small
d) none of the above

A

d) none of the above

90
Q

The mean is the most preferred measure of central tendency because

a) it is unaffected by outliers
b) it has an algebraic property
c) it is the easiest measure of central tendency to calculate
d) none of the above

A

b) it has an algebraic property

91
Q

Who wrote the article Statistical methods in psychology journals: Guidelines and explanations, summarizing the main problems of doing inferential statistics, and making suggestions for suitable options?

A

Wilkinson

92
Q

A decision to retain the null hypothesis means that you proved that the treatment has no effect. True or false?

A

False. failing to reject the null hypothesis does not prove it is true; there is jsut not enough evidence to reject it

93
Q

A Type I error is like convicting an innocent person in a jury trial. True or false?

A

True. Innocence is the null hypothesis for a jury trial; conviction is like rejecting that hypothesis.

94
Q

A Type II error is like convicting a guilty person in a jury trial. True or false?

A

False. Convicting a guilty person is not an error, but acquitting a guilty person would be like a Type II error

95
Q

An effect that exists is more likely to be detected if n is large. True or false?

A

False. A larger sample produces a smaller standard error, a larger z-score, but the effect is not more likely to be detected if n is large.

96
Q

An effect that exists is less likely to be detected if standard deviation is large. True or false?

A

the distance between means of two distributions represents the effect size, but it doesn’t change with sample size. Power will change if there is a larger sample size, making it easier to have significant results, but the effect size wil not be affected.

97
Q

Increasing the sample size will also increase the effect size. True or false?

A

False

98
Q

Lowering the alpha level from .05 to .01 will increase the power of a statitical test. True or false?

A

False. it is likely that the Ho will be rejected with a small alpha

99
Q

Which of the following is true about a 95% CI of the mean of a given sample:

a. 95 9UT OF 100 SAMPLE MEANS WILL FALL WITHIN the limits of the CI
b. There is a 95% chance that the pop mean will fall within the limits of the CI
c. 95 out of 100 pop means will fall within the limits of the CI
d. There is a .05 probability that the pop mean falls within the lmits of the CI

A

b. there is a 95% chance that the pop mean will fall within the limits of the CI

100
Q
  1. What does a significant test statistic tell us?
    a. There is an important effect
    b. The null hypothesis is false
    c. There is an effect in the pop of sufficient magnitude to be scientifically interesting
    d. All of the above
A

c. there is an effect in the population of sufficient magnitude to be scientifically interesting

101
Q
  1. A type I error is when
    a. We conclude that there is a meaningful effect in the pop when in fact there is not
    b. ..not a meaningful effect in the pop when in fact there is
    c. …the test stat is significant when in fact it is not
    d. The data we have typed into SPSS is different to the data collected
A

A

102
Q
  1. What is the conventional level of prob that is often accepted when conducting stat tests?
    a. .1
    b. .05
    c. .5
    d. .001
A

b

103
Q
  1. A null hypothesis
    a. States that the experimental treatment will have an effect
    b. Is rarely used in experiments
    c. Predicts that the experimental treatment will have no effect
    d. None of the above
A

c. predicts that the experimental treatment will have no effect

104
Q
  1. What does it mean when we reject the Ho at the .05 level?
    a. There is less than a 5% chance of getting such an extreme result by chance if the Ho is true
    b. There is more than a 5% chance…if the null hypothesis is true
    c. There is a 5% chance that there is a difference between the two pops, if the Ho is true
    d. There is a 95% chance that the research hypothesis is true
A

a. there is less than a 5% chance of getting suh an extreme result by chance if the Ho is true

105
Q
  1. The results of a study are not extreme enough to reject the Ho What can the person conclude with reasonable confidence?
    a. The results support
    b. None of the above – the results are inconclusive
    …
A

b. None of the above - the results are inconclusive

106
Q
  1. Failing to reject the Ho when the research hypothesis is true is referred to as
    a. The prob of rejection
    b. The error term
    c. A Type I
    d. A Type II
A

d. a Type II

107
Q
  • Decreases power
  • Increases power
  • Does not impact power
  • Cannot determine from information given
    9. Having a very small amount of overlap between the experimental and the comparison distribution.
A

increases power

108
Q
  • Decreases power
  • Increases power
  • Does not impact power
  • Cannot determine from information given

A smaller sample size

A

decreases power

109
Q
  • Decreases power
  • Increases power
  • Does not impact power
  • Cannot determine from information given

a larger population standard deviation

A

decreases power

110
Q
  • Decreases power
  • Increases power
  • Does not impact power
  • Cannot determine from information given

Using a more stringent significance level (.01 instead of .05)

A

decreases power

111
Q

Two purposes of inferential statistics

A

parameter estimation and hypothesis testing

112
Q

a sampling distribution is a frequency distribution of a

A

sample statistic

113
Q

the distribution of ___ means is usually referred to as the _____ distributions of ____ or, the ____ distribution of the mean

A

sample; sampling, means; sampling, mean

114
Q

a sampling distribution of means is a ____ distribution

A

probability

115
Q

it is the ____ ________distribution of means obtained from _____ sampling experiments, each consisting of a sample of size ___ that are ____ selected from the _______

A

relative frequency; unlimited series of; n; randomly; population

116
Q

What is the designation for sampling distribution of the mean?

A

miu with small x and _ above it

117
Q

What is the designation for sampling distribution of the standard deviation?

A

Greek o with a small x and __ above it

118
Q

If we sample n observations from a normally distributed population, the sampling distribution of the mean will be a ____ distribution

A

normal

119
Q

What is the Central Limit Theorem?

A

the distribution of sample means from samples of n observations will approach a normal distribution with standard deviation of ox = o/ (square root of(nP) - usually called the standard error of the mean) and mean of ux = u as n gets larger.

i.e. the larger the n (usually over 30), the closer the sampling distribution of x__ to a normal distribution

120
Q

In a ___ with standard deviation o and mean u, the distribution of sample means from samples of n observations will approach a ___ distribution with standard deviation of o x_ = o/ square root of (n) and mean of ux_ = u as n gets larger. What is this describing?

A

the central limit theorem

121
Q

the larger the n, the closer the ___ of ___ to a normal distribution

A

sampling distribution of x_

122
Q

If we sample __ observations from a normally distributed population, the sampling distribution of __ will be a normal distribution

A

n; x_

123
Q

What are the population parameter units?

A

o for standard deviation and u for mean

124
Q

what is the formula for standard error of the mean?

A

ox_ = o/square root of (n)

125
Q

The standard error of the mean is not affected by the sample size

A

false. The larger the n, the closer the sampling distribution of x_ to a normal distribution

126
Q

As the standard error of the mean gets larger, what does that usually say about the sample size?

A

it likely gets smaller

127
Q

If the variance if wide, what does that usually say about the risk of Type I error?

A

Type I error is likely to be larger, however Type II error will likely be smaller

128
Q

What is a Type I error?

A

rejecting the null hypothesis when it is true

e.g. convicting an innocent man

129
Q

What is a Type II error?

A

failing to reject the null hypothesis when it is false

e.g. letting an guilty man walk

130
Q

The probability of a Type I error in hypothesis testing is predetermine by the…

A

significance level

131
Q

The probability of a Type II error cannot generally be computed because it depends on the _____ ____ which is unknown. It can be computed at, however, for given values of ___, ___, and ___.

A

population mean;
u
o^2
n

132
Q

The ___ of a hypothesis test is nothing more than 1 minus the probability of a Type __ error. Basically, the ___ of a test is the probability that we make the right decision when the null is not correct (i.e. we correctly reject it).

A

power

Type II error

133
Q

If the Type II error rate gets larger, what happens to the power and Type I error rates?

A

they get smaller

power = 1 - B
Type I error is the opposite of Type II as well

134
Q

If the Type I error rate gets larger, what happens to the power and the Type II error rates?

A

the power likely increases, and the Type II error rate decreases

135
Q

If the power increases, what is likely happening with the error rates?

A

the Type I error rate likely increases, whereas the Type II error rate decreases

136
Q

What is the standard deviation of the distribution of sample means?

A

the standard error of the mean

137
Q

what is the standard error of the mean?

A

the standard deviation of the distribution of sample means

138
Q

when the standard error of the mean is large, what does it say about the means?

A

they are widely scattered

139
Q

if the means are narrowly scattered, what does that say about the standard error of the mean?

A

it is likely small

140
Q

What provides a measure of how much distance is expected on average between sample means and u?

A

the standard error of the mean

141
Q

What is the logic of inferential statistics?

A

p(D / Ho) or, what can we get from sampling distributions and samples, given the null hypothesis

Bayesian theory –
p(Ho/D) - what do we really want? Given the data, what can we assume about the null hypothesis?

142
Q

What did Neyman and Pearson introduce?

A

the concepts of Type I, type II errors, and statistical power

143
Q

What is a Type I error symbol?

A

Greek a

the probability of falsely rejecting the complementary yopthesis when ti is true

144
Q

What is a Type II error and its symbol?

A

B, and it is the prob of not rejecting the complementary hypothesis when it is false

145
Q

What is power, and what is its symbol?

A

1 - B, and it is the prob of a test to reject the complementary hypothesis when it is false

i.e. the chance of being too passive about your findings

146
Q

IF we accept the Ho assuming it is true, and the Ho is true, what is the consequence?

A

the correct decision

147
Q

If we accept the Ho and assume it is true, but the alternate hypothesis is shown to be true, what is the consequence?

A

type II error (probability B)

148
Q

If we reject the Ho and assume the alternate hypothesis is true, and the Ho is shown to be true, what is the consequence?

A

a Type I error (probability a)

149
Q

If we reject the Ho (assuming the alternate hypothesis is true) and the alternate hypothesis is shown to be true, what is the consequence?

A

the correct decision

150
Q

What are we looking for when measuring the sampling distribution of X_ under Ho and H1?

A

the distance between uo and u1, and whether the u1 is in the critical value/highly improbable area of the u0 curve. IF it is so highly improbable, then we assume that the chance of H1 happening is statistically significant, and highly unlikely to have occurred by chance

151
Q

What are factors that affect the power of a test?

A 
the size of a
1 or 2 tailed test
separation of u0 and u(effect size)
size of the pop variance (o^2)
the sample size, n
152
Q

What are the conflicts between the two schools of thought for statistics?

A

the number of hypotheses and the goal of hypothesis testing

153
Q

what is typically shown in textbooks?

A

a “unified” approach towards null hypothesis statistical testing appears without introducing the differences between the two schools

154
Q

Who has claimed authorship of NHST?

A

no one

155
Q

What is the unified approach of hypothesis testing?

A

labelling the two hypotheses as Ho and H1 (both for Neyman and Pearson’s and Fisher’s test of significance)

156
Q

How did Neyman and Pearson and Fisher differ in their presentation of the null hypothesis?

A

Neyman and Pearson presented it as a null relationship (no effect), whereas Fisher intended the null hypothesis as simply being something to be “nullified”

157
Q

What is Neyman-Pearson’s mandatory two-outcome decision process for hypothesis testing?

A

comparing the obtained p-value of a test stat with the a priori selected probability

158
Q

What is the second step of the two-outcome decision process for hypothesis testing developed by Neyman and Pearson?

A

asterisks placed next to test stats indicating ranges of p-values, leading a kind of evaluation of the strength of the evidence, as in Fisher’s Test of Significance

159
Q

What is the null ritual?

A

Setup Ho of no difference and specify the research/alternative hypothesis. Use 5% as a conventional level to reject Ho, report significance at what level

160
Q

What is a logical flaw of the unified approach or null ritual?

A

almost a contradiction of the Ho does not imply that the Ho is almost false

161
Q

What is the misconception on probability of the unified approach or null ritual?

A

an absence of evidence is not evidence of its absence; NHST does not tell us the prob that we want to know, and an obtained p-value is not a prob of replication

162
Q

What is a misconception of the 2 hypotheses, or unified approach, or null ritual?

A

the effects of A and B are always different, and asking “are the effects different?” is not what Fisher suggested. Instead, it should be WHY these are different (my insinuation of this line of thought)

163
Q

What is a misconception of rejecting or failing to reject the null hypothesis of the null ritual?

A

non-significant results do not imply theoretically and practically unimportance

164
Q

Why is the standard alpha problematic?

A

it is an arbitrary amount set at .05 which assumes that anything above that is not significant. What about .06?

165
Q

What is a misconception of the function of sample size in the null ritual?

A

for a given p-value in astudy which rejects the Ho, larger sample sizes do not imply more reliable results

166
Q

Based on this data, is the statement true or false? independent t-test: t = 2.7, df = 18, p = .01.

You have a reliable experimental finding in the sense that if, hypothetically, the experiment was repeated a great number of times, you would obtain a significant result on 99% of occasions.

A

False. Although ist suggests that the experiment is scientifically significant, we cannot accurately show the frequency we would have this result

167
Q

Based on this data, is the statement true or false? independent t-test: t = 2.7, df = 18, p = .01.

You know, if you fail to reject the null hypothesis, the probability that you are making the right decision

A

False. It only indicates that the chance of having this significant result is worthwhile enough to show a reasonable effect as indicative of the alternate hypothesis, which questions the accuracy of the null hypothesis

168
Q

Based on this data, is the statement true or false? independent t-test: t = 2.7, df = 18, p = .01.

You can deduce the probability of the experimental hypothesis being false

A

False. One, this test shows evidence to a significant finding of the alternate hypothesis, but we cannot “deduce the probability” of anything being true. We in fact seem to show the null hypothesis as being questionable, and perhaps false

169
Q

Based on this data, is the statement true or false? independent t-test: t = 2.7, df = 18, p = .01.

You have absolutely proved your experimental hyopthesis (that there is a difference between the population means)

A

False. This suggests that the experimental hypothesis has a scientifically significant result that provides evidence to support the alternate hypothesis, but it cannot possibly prove anything

170
Q

Based on this data, is the statement true or false? independent t-test: t = 2.7, df = 18, p = .01.

You have found the probability of the null hypothesis being true

A

false. Even if finding “the probability of the __ hypothesis being true” statement were real, this would show evidence to suggest the null hypothesis is false, not true

171
Q

Based on this data, is the statement true or false? independent t-test: t = 2.7, df = 18, p = .01.

You have absolutely disproved the null hypothesis (that is, there is no difference between the population means)

A

False. Have you looked t the data? it suggests that there is in fact an effect between the sampling distribution of the mean and the supposed population mean, which suggests a potential effect of the experiment

172
Q

Are these statements acceptable as findings in scientific journals?

“the prob that an observed difference is real”
“the improb of observed results being due to error”
“the stat confidence…which odds of 95 out of 100 that…”
“the danger of accepting a stat result as real when it is actually due only to error”
“the degree to which experimental results are taken ‘seriously’”
“the degree of ‘faith that can be placed in the reality of the finding’”
“the investigator can have 95% confidence that the sample mean actually differs from the population mean”
“if the prob is low, the Ho is improbable”
“all of these are different ways to say the same thing”

A

Hell naw

173
Q

Who wrote FUNDAMENTAL STATISTICS IN PSYCHOLOGY EDUCATION (1942)?

A

Guilford

174
Q

“if the result comes out one way, the hypotehsis is probably correct, if it comes out another way, the hypothesis is probably wrong”. Who said this?

A

Guilford, in FUNDAMENTAL STATISTICS IN PSYCHOLOGY EDUCATION

175
Q

How can hypothesis testing be valid in true experiments?

A

when involving randomization

176
Q

true of false: There is no magical alternative to hypothesis testing.

A

True

177
Q

What are specific strategies of hypothesis testing?

A

calculate effect sizes, provide CIs, replicate findings, provide statistical/methodological advancements

178
Q

What is a point estimation?

A

a stat procedure that involves the use of a sample stat (e.g., a sample mean) to estimate a population parameter (e.g., a population mean)

179
Q

What is an advantage of point estimation?

A

it is an unbiased estimator, that is the sample mean will equal the population mean on average

180
Q

What is a disadvantage of point estimation?

A

We have no way of knowing for sure whether a sample mean equals the pop mean. For this reason, researchers often report a point estimate with an interval estimate (i.e. CI)

181
Q

Who wrote about the benefits of power?

A

Welkowitz, Ewen and Cohen (2000)

182
Q

What kind of approach is power considered as in null hypothesis testing?

A

a positive approach

183
Q

What is power?

A

the probability of a successful replication, or the % of the time that the researcher can expect a significant difference between the null hypothesis and the alternate hypothesis (the likelihood that s/he can reasonably reject the null hypothesis)

184
Q

What is power in a formula?

A

power = 1 - B (Type II error)

185
Q

When power goes up, what happens to the alpha?

A

it goes up

186
Q

When power goes up, what is the likelihood of the sample size?

A

it goes up

187
Q

If power goes up, what is the likelihood of the variance and standard deviation of the sampling distribution?

A

they go down

188
Q

If power goes up, what is the likelihood of the chance for Type II error?

A

it goes down

189
Q

What happens if power goes down?

A

likely the sample size decreases, the alpha rate is decreased, whereas the variation (variance or standard error) or Type II error go up

190
Q

How do you calculate effect size?

A

d = u1 - u0 / o

191
Q

What is effect size also known as?

A

Cohen’s d

192
Q

What is considered a small effect size, and what does it equate to power, and therefore the likelihood of rejecting the null hypothesis?

A

.2, which corresponds with a power of .90, which means that 90% of the time the researcher can expect a significant difference between the null hypothesis and the alternate hypothesis, which means a large chance of successfully rejecting the null hypothesis

193
Q

What is considered a medium effect size, and what does it equate to power, and therefore the likelihood of rejecting the null hypothesis?

A

.5, with a power of .80 which means that 80% of the time the researcher can expect a significant difference between the null hypothesis and the alternate hypothesis, which means there is a good chance of successfully rejecting the null hypothesis

194
Q

What is considered a large effect size, and what does it equate to power, and therefore the likelihood of rejecting the null hypothesis?

A

.8, which corresponds with a power of 69% of the time that the researcher can expect a significant difference between the null hypothesis and the alternate hypothesis, and so there is a fair chance of successfully rejecting the null hypothesis

195
Q

If your power is high, what is the likelihood of your effect size?

A

the effect size is likely small, which corresponds with the size of the alpha, which being small!

196
Q

If your power is increased, what happens to the other factors?

A

B - Type II error decreases
o - decreases
o^2 - decreases
d - effect size decreases

197
Q

If your power increases, what happens to the alpha and sample size?

A

the sample size likely goes up as does the alpha rate, increasing the area in which we can successfully reject the null hypothesis (whether it’s statistically significant is another thing)

198
Q

If you have a SD of 2, what does that mean?

A

if it is +1.96 or -1.96 (in a two-way test), the results are significant at .05

199
Q

If you have a sd of greater than 3, what does that mean

A

if it is +2.58 or -2.58 (in a two-way test) the results are significant at .01

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