NCE Research and Program Evaluation
Exam Questions and Answers 100% Pass
Parsimony (AKA Occam's Razor) - ✔✔interpreting results in the simplest way
Basic research - ✔✔advances our theory
Applied research - ✔✔advances our practices
Alternative Hypothesis - ✔✔states that the treatment or IV does affect the outcome of
the experiment.
Null Hypothesis - states that the treatment or IV will not have an affect
Between Subjects Design - ✔✔different subjects get exposure or lack of exposure to
different things
In Subjects Design - ✔✔one pool of subjects receive or don't receive the treatment (pre-
test post-test design)
Parameter - ✔✔a value obtained from a population; summarizes a characteristic of a
population (e.g., age, sex, average male height)
It is theorized that all population parameters "fit" the normal curve
Statistic - ✔✔A value drawn from a sample
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,Probability (aka significance level) - ✔✔the likelihood that something will happen,
Used in true experiments
P for our field is generally .05 but can range from .000 to .10 and still be considered
significant findings or not due to chance,
P can be translated into a percentage that describes the portion of the sample whose
results were achieved by chance (i.e. .05 = 5% of the sample's scores were obtained by
chance - not your experimental design)
.05 means that differences would occur via chance only 5 times in 100; the experimenter
will obtain the same results 95 times out of 100
May be referred to as confidence level in the exam
Type 1 error (aka alpha error) - ✔✔rejecting the null when it is true (saying there is
significance in your treatment when there isn't),
False positive
The probability of committing a Type 1 error = the level of significance (P)
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, HINT: Alphas want power and will want to win even if that means faking a win
Type II Error (Beta Error) - ✔✔accepting the null when it is not true (saying there isn't
significance in your treatment when there is),
False negative
How do you reduce Type 1 AND Type 2 errors? - ✔✔Increase sample size
How do you reduce Type 1 errors but increase chance of Type 2 Errors? - ✔✔decrease P
levels
T-Test - ✔✔determines if a significant difference between two means exist; t value
statistic has to be higher than the t value in the table to be significant
Uses mean of 50 with each SD as 10 (Hence, a Z score of -1 would be a t-score of 40)
used for two samples to compare means, you obtain a single t score and compare it to
the critical t value based on the sample size and your significance level and if the t value
you found is greater than the critical t you have significance
for 2 groups
The values of a t-test are determined by degrees of freedom (influenced by sample size).
Analysis of variance
ANOVA - ✔✔F Statistic
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Exam Questions and Answers 100% Pass
Parsimony (AKA Occam's Razor) - ✔✔interpreting results in the simplest way
Basic research - ✔✔advances our theory
Applied research - ✔✔advances our practices
Alternative Hypothesis - ✔✔states that the treatment or IV does affect the outcome of
the experiment.
Null Hypothesis - states that the treatment or IV will not have an affect
Between Subjects Design - ✔✔different subjects get exposure or lack of exposure to
different things
In Subjects Design - ✔✔one pool of subjects receive or don't receive the treatment (pre-
test post-test design)
Parameter - ✔✔a value obtained from a population; summarizes a characteristic of a
population (e.g., age, sex, average male height)
It is theorized that all population parameters "fit" the normal curve
Statistic - ✔✔A value drawn from a sample
100% Pass Guarantee Emily Charlene All Rights Reserved © 2025 1
,Probability (aka significance level) - ✔✔the likelihood that something will happen,
Used in true experiments
P for our field is generally .05 but can range from .000 to .10 and still be considered
significant findings or not due to chance,
P can be translated into a percentage that describes the portion of the sample whose
results were achieved by chance (i.e. .05 = 5% of the sample's scores were obtained by
chance - not your experimental design)
.05 means that differences would occur via chance only 5 times in 100; the experimenter
will obtain the same results 95 times out of 100
May be referred to as confidence level in the exam
Type 1 error (aka alpha error) - ✔✔rejecting the null when it is true (saying there is
significance in your treatment when there isn't),
False positive
The probability of committing a Type 1 error = the level of significance (P)
100% Pass Guarantee Emily Charlene All Rights Reserved © 2025 2
, HINT: Alphas want power and will want to win even if that means faking a win
Type II Error (Beta Error) - ✔✔accepting the null when it is not true (saying there isn't
significance in your treatment when there is),
False negative
How do you reduce Type 1 AND Type 2 errors? - ✔✔Increase sample size
How do you reduce Type 1 errors but increase chance of Type 2 Errors? - ✔✔decrease P
levels
T-Test - ✔✔determines if a significant difference between two means exist; t value
statistic has to be higher than the t value in the table to be significant
Uses mean of 50 with each SD as 10 (Hence, a Z score of -1 would be a t-score of 40)
used for two samples to compare means, you obtain a single t score and compare it to
the critical t value based on the sample size and your significance level and if the t value
you found is greater than the critical t you have significance
for 2 groups
The values of a t-test are determined by degrees of freedom (influenced by sample size).
Analysis of variance
ANOVA - ✔✔F Statistic
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