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statistics vs. parameters - 🧠 ANSWER ✔✔analysis on a sample vs. analysis on the
entire population
overview to data analysis - 🧠 ANSWER ✔✔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 - 🧠 ANSWER ✔✔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)
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,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 - 🧠 ANSWER ✔✔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... - 🧠 ANSWER ✔✔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 - 🧠 ANSWER ✔✔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 - 🧠 ANSWER ✔✔correlation
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,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 - 🧠 ANSWER ✔✔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 - 🧠 ANSWER ✔✔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) - 🧠 ANSWER ✔✔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 - 🧠 ANSWER ✔✔RANOVA (parametric) or Friedman
(non-parametric)
how can you choose? - 🧠 ANSWER ✔✔what is the research question or hypothesis
asking?
are groups independent or dependent?
what is the level of measurement of the variables?
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, are the assumptions of the statistical test met, particularly that of normal
distribution?
knowledge of statistics is necessary to all who read or conduct research - 🧠
ANSWER ✔✔research: making observations or measurements on people, things,
or events to answer a research question
statistics: a set of procedures for describing those measurements--how do we
quantify and report these measurements?
data - 🧠 ANSWER ✔✔the raw material of research
variable - 🧠 ANSWER ✔✔something that varies or takes on different values
we are always interested in variability and explaining variation
variables are also classified as... - 🧠 ANSWER ✔✔discrete: finite number of value
(almost like you can count)--obtained by counting
continuous: infinite number of value between any two points--obtained by
measuring
statistical methods are sometimes described by the number of variables in the
analysis - 🧠 ANSWER ✔✔univariate: average cholesterol
bivariate: average cholesterol (exercise group and sedentary group)
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PRIVACY STATEMENT. ALL RIGHTS RESERVED