don’t want the reader to look at.
You don’t necessarily need to answer a question in paragraph form . A bulleted list or algebraic
demonstration may work well if you are comfortable doing it that way.
- Understand that Question #6, the investigative task, may contain questions about material you’ve
never studied . The goal of such a question is to see how well you think statistically in a situation for
which you have no rote answer. Unlike every other question on the test, you really don’t need to
worry about preparing for this question beyond normal test preparation and being sure that you
understand as much of the material in the course as possible.
Specific Statistics Content Tips
The following set of tips are things that are most worth remembering about specific content issues in
statistics. These are things that have been consistent over the years of the reading. This list is not
exhaustive! The examples, exercises, and solutions that appear in this book are illustrative of the manner
in which you are expected to answer free-response problems, but this list is just a sampling of some of the
most important things you need to remember.
When asked to describe a one-variable data set, always discuss shape, center, and spread.
- If you are asked to compare distributions, use phrases such as greater than, less than , and the same
as . - Understand how skewness can be used to differentiate between the mean and the median.
- Know how transformations of a data set affect summary statistics.
- Be careful when using “normal” as an adjective. Normal refers to a specific distribution, not the
general shape of a graph of a data set. It’s better to use “approximately normal,” “mound-shaped and
symmetric,” etc., instead. You will be docked for saying something like, “The shape of the data set is
normal.” - Remember that a correlation does not necessarily imply a causal relationship between two variables.
Conversely, the absence of a strong correlation does not mean there is no relationship (it might not be
linear). - Be able to use a residual plot to help determine if a linear model for a data set is appropriate. Be
able to explain your reasoning. - Be able to interpret, in context, the slope and y -intercept of a least-squares regression line.
- Be able to read computer regression output.
- Know the definition of a simple random sample (SRS).
- Be able to design an experiment using a completely randomized design. Understand that an
experiment that utilizes blocking cannot, by definition, be a completely randomized design. - Know the difference between the purposes of randomization and blocking.
- Know what blinding and confounding variables are.
- Know how to create a simulation for a probability problem.
- Be clear on the distinction between independent events and mutually exclusive events (and why
mutually exclusive events can’t be independent). - Be able to find the mean and standard deviation of a discrete random variable.
- Recognize binomial and geometric situations.
- Never forget that hypotheses are always about parameters, never about statistics.