**APMA0650 or APMA1650, which is the right course?**

**There
is a big difference. **

**APMA0650**

*Essential Statistics* is an
introductory first course intended for students from a variety of
disciplines.

The goals of APMA0650 are to that provide the
framework for statistical reasoning and statistical judgment and to provide an
understanding of the
connections between probability and statistics, the basic principles of
estimation and confidence intervals, the philosophy and technical definition of
hypothesis testing, and a handful of the most commonly used methods of
estimation and hypothesis testing. The course emphasizes an understanding
of statistical principles but is light on mathematical foundations and
derivations.

The coverage of APMA0650 is not substantially
different from a high-school course in AP Statistics. (Students who have mastered high-school AP
Statistics are advised not to take APMA0650.)
The level is somewhat more technical than the usual AP Statistics
course, but accessible to any Brown student who is comfortable with high-school
algebra. APMA0650 can be taken by
non-science majors. It is not a pre-requisite
for APMA1650, but students who have taken APMA0650 can go on to take APMA1650
for a more comprehensive and mathematical treatment.

**APMA1650**

In contrast, APMA1650 (*Statistical Inference I*) is a rigorous first course in probability
and mathematical statistics with applications. The course includes a
self-contained introduction to probability theory, and statistical methods are
derived from their probabilistic roots. Applications of these principles
to textbook problems and active research problems at Brown are also an essential
component of the course. The course
provides the background and is a prerequisite for many other courses concerning
probability and statistics in the Division of Applied Mathematics, and other
departments. The successful student will be in a good position to understand
new and more complex statistical methods, to critically evaluate the strengths,
weaknesses and the appropriateness of existing statistical methodologies. They
will also have the foundation to learn to apply these principles in new settings.