Common Mistakes in Using Statistics - Spotting Them and Avoiding
Them
2013 Summer Statistics Institute Course, University of Texas at
Austin
May 20 - 23, 2013
Course Notes
Please
Note:
- Files are in pdf format.
- Most students will want to download the slides and either print them to
take notes on in class, or follow along in class on their
laptop.
- The appendices contain
additional material and references. They are available for your
reference later according to your own needs.
- Copies of course materials will not
be handed out in class.
- Computers for individual use will not be available in the classroom
for this particular course.
- If you need a different print size or would prefer a .doc file to
take notes on, please email me so
I can email you .doc files to adjust
to your needs. (Past experience is that .doc files often acquire
changes in formatting or symbols when posted on the web; this might
also happen with email attachments on some platforms.)
External Links
Empirical
Probability Example
Wise Sampling
Distribution Simulation
Rice
Virtual Lab in Statistics Sampling Distribution Simulation
Bioconsulting
Confidence Interval Simulation
R.
Webster's Confidence Interval Simulation
W.
H Freeman's Confidence Interval Simulation
The
Rice Virtual Lab in Statistics Confidence Interval Simulation
Rice
Virtual Lab in Statistics Robustness Simulation
Claremont
University's Wise Project's Statistical Power Applet
Jerry Dallal's
Simulation of Multiple Testing
This simulates the results of 100
independent hypothesis tests, each at 0.05 significance level. Click
the "test/clear" button to see the results of one set of 100
tests (that is, for one sample of data). Click the button two more
times (first to clear and then to do another simulation) to see the
results of another set of 100 tests (i.e., for another sample of data).
Notice as you continue to do this that i) which tests give type I
errors (i.e., are statistically significant at the 0.05 level) varies
from sample to sample, and ii) which samples give type I errors for a
given test varies from test to test. (To see the latter point, it may
help to focus just on the first column.)
Jelly Beans (A Folly of Multiple
Testing and Data Snooping)
Negative
Consequences of Dichotomizing Continuous Predictor Variables
p-value video (amusement)
Content similar to the content of the course notes,
but includes embedded links and more information.
A companion to the preceding
website Common Mistakes in Using Statistics. It contains updates to
that site and occasional comments on other things related to statistics
that come to my attention. It may be of interest to the following
categories of people:
Teachers of statistics (especially those, such as
myself, who come from backgrounds other than statistics)
Undergraduates and early graduate students in
statistics
Users of statistics (especially people who read
research using statistics)
Last updated May 17, 2013