students_t_test.ppt

Student’s t Test
Ibrahim A. Alsarra, Ph.D.
King Saud University
College of Pharmacy
Department of Pharmaceutics
PHT 463: Quality Control of Pharmaceutical Dosage Forms
Summer Semester of 1423-1424H

Statistical Estimation and Hypothesis
Testing
 Parameters estimates from samples are meant to be used
to estimate the true population parameters.
 The sample mean and variance are typical estimators or
predictors of the true mean and variance.

Introduction
 "Student" (real name: W. S. Gossett [1876-1937])
developed statistical methods to solve problems
stemming from his employment in a brewery.
 Student's t-test deals with the problems associated
with inference based on "small" samples: the
calculated mean and standard deviation may by
chance deviate from the "real" mean and standard
deviation (i.e., what you'd measure if you had many
more data items: a "large" sample).

Introduction
 'Student's' t Test is one of the most
commonly used techniques for testing a
hypothesis on the basis of a difference
between sample means. Explained in
layman's terms, the t test determines a
probability that two populations are the
same with respect to the variable tested.

Criteria
 If there is a direct relationship between specific data
points from two data sets, such as measurements on the
same subject 'before and after,' then the paired t test MAY
be appropriate.
 If samples are collected randomly from two different
populations or from different individuals within the same
population at different times, use the test for independent
samples (unpaired).
 Here's a simple way to determine if the paired t test can
apply - if one sample can have a different number of data
points from the other, then the paired t test cannot apply.

'Student's' t Test (For Paired
Samples)
 The number of points in each data set must be
the same, and they must be organized in pairs,
in which there is a definite relationship
between each pair of data points
 If the data were taken as random samples, you
must use the independent test even if the
number of data points in each set is the same
 Even if data are related in pairs, sometimes the
paired t is still inappropriate

Example
 The paired t test is generally used when measurements
are taken from the same subject before and after some
manipulation such as injection of a drug. For example,
you can use a paired t test to determine the significance
of a difference in blood pressure before and after
administration of an experimental pressure substance.
You can also use a paired t test to compare samples that
are subjected to different conditions, provided the
samples in each pair are identical otherwise. For
example, you might test the effectiveness of a water
additive in reducing bacterial numbers by sampling water
from different sources and comparing bacterial counts in
the treated versus untreated water sample. Each different
water source would give a different pair of data points.

Remember that…
 The t-test assesses whether the means of two groups
are statistically different from each other.
 This analysis is appropriate whenever you want to
compare the means of two groups

Figure 1. Idealized distributions for
treated and comparison group post-
test values
 Figure 1 shows the
distributions for the treated
(blue) and control (green)
groups in a study. Actually,
the figure shows the
idealized distribution -- the
actual distribution would
usually be depicted with a
histogram or bar graph. The
figure indicates where the
control and treatment group
means are located. The
question the t-test
addresses is whether the
means are statistically
different.

What does it mean to say that the averages
for two groups are statistically different?

Figure 2. Three scenarios for differences
between means
 Consider the three situations
shown in Figure 2. The first
thing to notice about the
three situations is that the
difference between the
means is the same in all
three. But, you should also
notice that the three
situations don't look the
same -- they tell very
different stories. The top
example shows a case with
moderate variability of
scores within each group.

Figure 2. Three scenarios for
differences between means …cont.
 The second situation shows the
high variability case. the third
shows the case with low variability.
Clearly, we would conclude that the
two groups appear most different
or distinct in the bottom or low-
variability case. Why? Because
there is relatively little overlap
between the two bell-shaped
curves. In the high variability case,
the group difference appears least
striking because the two bell-
shaped distributions overlap so
much.

 This leads us to a very important
conclusion: when we are looking at the
differences between scores for two groups,
we have to judge the difference between
their means relative to the spread or
variability of their scores. The t-test does
just this.

Statistical Analysis of the t-test
 The formula for the t-test is a ratio. The top
part of the ratio is just the difference between
the two means or averages. The bottom part is
a measure of the variability or dispersion of the
scores. This formula is essentially another
example of the signal-to-noise metaphor in
research: the difference between the means is
the signal that, in this case, we think our
program or treatment introduced into the data;
the bottom part of the formula is a measure of
variability that is essentially noise that may
make it harder to see the group difference

Figure 3 shows the formula for the t-test and how the
numerator and denominator are related to the
distributions

The top part of the formula is easy to compute -- just find
the difference between the means. The bottom part is
called the standard error of the difference. To compute
it, we take the variance for each group and divide it by the
number of people in that group. We add these two values
and then take their square root. The specific formula is
given in Figure 4:
Figure 4. Formula for the Standard error of
the difference between the means.

 Remember, that the variance is simply the
square of the standard deviation.
 The final formula for the t-test is shown in
Figure 5:
Figure 5. Formula for the t-test

Finally
 The t-value will be positive if the first mean is larger
than the second and negative if it is smaller. Once
you compute the t-value you have to look it up in a
table of significance to test whether the ratio is large
enough to say that the difference between the
groups is not likely to have been a chance finding. To
test the significance, you need to set a risk level
(called the alpha level). In most social research, the
"rule of thumb" is to set the alpha level at .05.

Finally
 This means that five times out of a hundred you
would find a statistically significant difference
between the means even if there was none (i.e., by
"chance"). You also need to determine the degrees
of freedom (df) for the test. In the t-test, the degrees
of freedom is the sum of the persons in both groups
minus 2. Given the alpha level, the df, and the t-
value, you can look the t-value up in a standard table
of significance (available as an appendix in the back
of most statistics texts) to determine whether the t-
value is large enough to be significant.

Finally
 If it is, you can conclude that the difference between
the means for the two groups is different (even given
the variability). Fortunately, statistical computer
programs routinely print the significance test results
and save you the trouble of looking them up in a
table.

students_t_test.ppt

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