STT 231 COMPLETE QUESTIONS
WITH CORRECT SLUTIONS||100%
GUARANTEED PASS||UPDATED
2026/2027 SYLLABUS||ALREADY
GRADED A+||
what is the null distribution? - ANSWER ✓ the sampling distribution based off of
the null value?
p-value - ANSWER ✓ assuming the null hypothesis is true, the probability that
you will observe data as favorable or more favorable for the alternative hypothesis
as the current observed data
what do high and low p values mean in a general sense? - ANSWER ✓ a high p-
value :0.10 means that 10% of the time, you could expect to see your sample
statistic occur under the null model, lower values 0.05 means that only 5% of the
time would the null distribution support your sample statistic
what are the various cutoff values for a p value? - ANSWER ✓ less than 0.001
(extremely strong evidence against null)
0.001 to 0.01 (very strong evidence)
0.01 to 0.05 (strong evidence)
0.05 to 0.10 (some evidence)
more than 0.10 (little evidence against null hypothesis)
what is a z-score - ANSWER ✓ number of standard deviations away from the
mean
one-proportion z test? - ANSWER ✓ describes the position of data in terms of the
mean (0) and is measured in standard deviations. Basically the number of standard
deviations/standard errors a certain data point is from the mean of the null
distribution
, difference between p values and z scores - ANSWER ✓ z score compares a
current data point to the data point's mean of the null model (0), and a p value
would quantify the probability of getting that data point under the null model
how to interpret large/small z-score values - ANSWER ✓ large z score: sample
data is either larger/smaller than the null model
small z score: null model can be a correct representation because their is little
different in terms of standard error between your observed sample and the null
model
when is Cohen's H used? - ANSWER ✓ when you want to gauge how
wrong/right the null model is for a single-proportion test
what is the relationship between p value and effect size? - ANSWER ✓ as the
effect size becomes more extreme either positively or negatively, the p value
decreases, because the null model becomes more wrong
which statistics consider n, and which do not? - ANSWER ✓ sample size
considered for p value and test statistiscs, but not for effect size
what are the 4 magnitudes for Cohen's H values? - ANSWER ✓ less than 0.2: null
model wrong but can still be used as a "correct model"
0.2 to 0.5: null model wrong but only proved through statistical testing
0.5 to 0.8: trained observer would notice null is incorrect
greater than 0.8: null model is so wrong it is noticeable with the naked eye
what conditions need to be met in order for the CLT to apply for mean sampling? -
ANSWER ✓ 1: the model must be normally distributed
2: the sample size must be sufficiently large
3: the cases must be independent and identically distributed
why do we use s instead of sigma for t-distributions? - ANSWER ✓ because if we
are estimating mu, there is an even slimmer likelihood we would know the value of
sigma, and sigma introduces additional variability N(0,1) cannot handle
what is degrees of freedom? - ANSWER ✓ maximum number of free samples of
the data, found by subtracting 1 from n
WITH CORRECT SLUTIONS||100%
GUARANTEED PASS||UPDATED
2026/2027 SYLLABUS||ALREADY
GRADED A+||
what is the null distribution? - ANSWER ✓ the sampling distribution based off of
the null value?
p-value - ANSWER ✓ assuming the null hypothesis is true, the probability that
you will observe data as favorable or more favorable for the alternative hypothesis
as the current observed data
what do high and low p values mean in a general sense? - ANSWER ✓ a high p-
value :0.10 means that 10% of the time, you could expect to see your sample
statistic occur under the null model, lower values 0.05 means that only 5% of the
time would the null distribution support your sample statistic
what are the various cutoff values for a p value? - ANSWER ✓ less than 0.001
(extremely strong evidence against null)
0.001 to 0.01 (very strong evidence)
0.01 to 0.05 (strong evidence)
0.05 to 0.10 (some evidence)
more than 0.10 (little evidence against null hypothesis)
what is a z-score - ANSWER ✓ number of standard deviations away from the
mean
one-proportion z test? - ANSWER ✓ describes the position of data in terms of the
mean (0) and is measured in standard deviations. Basically the number of standard
deviations/standard errors a certain data point is from the mean of the null
distribution
, difference between p values and z scores - ANSWER ✓ z score compares a
current data point to the data point's mean of the null model (0), and a p value
would quantify the probability of getting that data point under the null model
how to interpret large/small z-score values - ANSWER ✓ large z score: sample
data is either larger/smaller than the null model
small z score: null model can be a correct representation because their is little
different in terms of standard error between your observed sample and the null
model
when is Cohen's H used? - ANSWER ✓ when you want to gauge how
wrong/right the null model is for a single-proportion test
what is the relationship between p value and effect size? - ANSWER ✓ as the
effect size becomes more extreme either positively or negatively, the p value
decreases, because the null model becomes more wrong
which statistics consider n, and which do not? - ANSWER ✓ sample size
considered for p value and test statistiscs, but not for effect size
what are the 4 magnitudes for Cohen's H values? - ANSWER ✓ less than 0.2: null
model wrong but can still be used as a "correct model"
0.2 to 0.5: null model wrong but only proved through statistical testing
0.5 to 0.8: trained observer would notice null is incorrect
greater than 0.8: null model is so wrong it is noticeable with the naked eye
what conditions need to be met in order for the CLT to apply for mean sampling? -
ANSWER ✓ 1: the model must be normally distributed
2: the sample size must be sufficiently large
3: the cases must be independent and identically distributed
why do we use s instead of sigma for t-distributions? - ANSWER ✓ because if we
are estimating mu, there is an even slimmer likelihood we would know the value of
sigma, and sigma introduces additional variability N(0,1) cannot handle
what is degrees of freedom? - ANSWER ✓ maximum number of free samples of
the data, found by subtracting 1 from n
