N580 FINAL EXAM WITH CORRECT
QUESTIONS AND ANSWERS 2025
statistics vs. parameters - CORRECT-ANSWERS*********analysis on a sample vs. analysis
on the entire population
overview to data analysis - CORRECT-ANSWERS*********quantitative: descriptive and
inferential
descriptive: simply describe a characteristic of a population/sample or a phenomenon
inferential: hope to generalize from a sample to a population (based on probability)--we use
parametric and non-parametric statistics
parametric tests and non-parametric tests - CORRECT-ANSWERS*********every statistical
test has assumptions that must be met
parametric tests have more stringent assumptions--two of the most critical assumptions:
normal distribution of the data and level of measurement (must be interval-like in nature)
,non-parametric tests don't have such limitations
we like parametric tests b/c they are more powerful and more likely to find a difference if
there is one
one chosen will depend on your data
analysis - CORRECT-ANSWERS*********clearly the problem is the driving force--the
sophistication of analysis will never compensate for an insignificant problem
always remember the research question or hypothesis always drives the analysis
look at the hypothesis or question--should be able to begin to think about the type of analysis
that would be appropriate
when you are beginning to think about statistical tests... - CORRECT-
ANSWERS*********you must look at what drives the tests-->the research question and the
hypothesis--what are the variables and how are they measured? what is the level of
measurement?
,if the question focuses on describing some phenomena - CORRECT-ANSWERS*********N &
% (if data are nominal or categorical) or descriptive statistics (if measures are interval-like in
nature--mean, standard deviation, range)
if the research question or hypothesis is interested in a relationship between two variables -
CORRECT-ANSWERS*********correlation
Pearson's r (parametric test) or Spearman Rho/Kendall's Tau (non-parametric test)
if the research question or hypothesis is interested in a difference between two independent
groups - CORRECT-ANSWERS*********the independent t test (parametric) or the Mann
Whitney U (if data weren't normally distributed)
if the research question or hypothesis is interested in a difference between two or more
independent groups - CORRECT-ANSWERS*********ANOVA (parametric) or the Kruskal
Wallis (if data aren't normally distributed or if measure is ordinal)
, if the research question or hypothesis is interested in differences in two groups that are
dependent (repeated measures) - CORRECT-ANSWERS*********the paired t test
(parametric) or Wilcoxon (non-parametric)
if the research question or hypothesis is interested in the difference in two or more groups
that are dependent - CORRECT-ANSWERS*********RANOVA (parametric) or Friedman
(non-parametric)
how can you choose? - CORRECT-ANSWERS*********what is the research question or
hypothesis asking?
are groups independent or dependent?
what is the level of measurement of the variables?
are the assumptions of the statistical test met, particularly that of normal distribution?
QUESTIONS AND ANSWERS 2025
statistics vs. parameters - CORRECT-ANSWERS*********analysis on a sample vs. analysis
on the entire population
overview to data analysis - CORRECT-ANSWERS*********quantitative: descriptive and
inferential
descriptive: simply describe a characteristic of a population/sample or a phenomenon
inferential: hope to generalize from a sample to a population (based on probability)--we use
parametric and non-parametric statistics
parametric tests and non-parametric tests - CORRECT-ANSWERS*********every statistical
test has assumptions that must be met
parametric tests have more stringent assumptions--two of the most critical assumptions:
normal distribution of the data and level of measurement (must be interval-like in nature)
,non-parametric tests don't have such limitations
we like parametric tests b/c they are more powerful and more likely to find a difference if
there is one
one chosen will depend on your data
analysis - CORRECT-ANSWERS*********clearly the problem is the driving force--the
sophistication of analysis will never compensate for an insignificant problem
always remember the research question or hypothesis always drives the analysis
look at the hypothesis or question--should be able to begin to think about the type of analysis
that would be appropriate
when you are beginning to think about statistical tests... - CORRECT-
ANSWERS*********you must look at what drives the tests-->the research question and the
hypothesis--what are the variables and how are they measured? what is the level of
measurement?
,if the question focuses on describing some phenomena - CORRECT-ANSWERS*********N &
% (if data are nominal or categorical) or descriptive statistics (if measures are interval-like in
nature--mean, standard deviation, range)
if the research question or hypothesis is interested in a relationship between two variables -
CORRECT-ANSWERS*********correlation
Pearson's r (parametric test) or Spearman Rho/Kendall's Tau (non-parametric test)
if the research question or hypothesis is interested in a difference between two independent
groups - CORRECT-ANSWERS*********the independent t test (parametric) or the Mann
Whitney U (if data weren't normally distributed)
if the research question or hypothesis is interested in a difference between two or more
independent groups - CORRECT-ANSWERS*********ANOVA (parametric) or the Kruskal
Wallis (if data aren't normally distributed or if measure is ordinal)
, if the research question or hypothesis is interested in differences in two groups that are
dependent (repeated measures) - CORRECT-ANSWERS*********the paired t test
(parametric) or Wilcoxon (non-parametric)
if the research question or hypothesis is interested in the difference in two or more groups
that are dependent - CORRECT-ANSWERS*********RANOVA (parametric) or Friedman
(non-parametric)
how can you choose? - CORRECT-ANSWERS*********what is the research question or
hypothesis asking?
are groups independent or dependent?
what is the level of measurement of the variables?
are the assumptions of the statistical test met, particularly that of normal distribution?
