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0:00
Hello, my name is
Benjamin Leiby.
I am a Professor
of Biostatistics
at Thomas Jefferson University.
Today I will be
introducing the basics of
sample size calculations in
experimental and
non-experimental settings.
0:14
One of the questions
statisticians are
often asked is how many people
do I need to study to be able to
have a statistically
valid conclusion?
My objectives today are
to address this question
and to review the basics of
statistical hypothesis testing,
to define Type 1
and Type 2 errors,
to show what affects
power and sample size,
to demonstrate common power
and sample size calculations.
In this talk, I'm
going to focus on
two-group comparisons.
For example, comparing
people who receive
a treatment to those who do not.
I will be discussing this in
the context of
biomedical applications
although the concepts
can be applied to
any setting where two
groups are being compared.
0:55
First, let's review the basics of
statistical hypothesis testing.
A foundational distinction
in statistical thinking
is between the population
and the sample.
The population is the large,
universal set of all people,
or animals, or things,
about which we wish
to answer a question.
For example, all people
with lung cancer,
or all people with stage
three lung cancer.
The population can be
broad or specific,
but it is not able to
be completely measured
or completely studied.
In order to answer questions
about the population,
we study a sample
from that population.
We want to draw the sample well,
so that we can actually make
a conclusion about
the population.
Part of having a good
sample is how it is drawn
so that it is representative
of the population.
The other important part,
which is the subject
of today's talk,
is the size of the sample.
We want the sample
to be big enough
to have a precise estimate.
We want to balance that against
the costs of doing research.
A study that is too large
will cost more financially,
but also could expose patients
to risk unnecessarily.
Based on the data we
collect from the sample,
we will test a hypothesis and
then using the results of
that test that we perform with
the data from the sample,
we draw our conclusions
about the population.
This process is called
statistical testing
or statistical inference.
