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[section:st_eg Student's t Distribution Examples]
[section:tut_mean_intervals Calculating confidence intervals on the mean with the Students-t distribution]
Let's say you have a sample mean, you may wish to know what confidence intervals
you can place on that mean. Colloquially: "I want an interval that I can be
P% sure contains the true mean". (On a technical point, note that
the interval either contains the true mean or it does not: the
meaning of the confidence level is subtly
different from this colloquialism. More background information can be found on the
[@http://www.itl.nist.gov/div898/handbook/eda/section3/eda352.htm NIST site]).
The formula for the interval can be expressed as:
[equation dist_tutorial4]
Where, ['Y[sub s]] is the sample mean, /s/ is the sample standard deviation,
/N/ is the sample size, /[alpha]/ is the desired significance level and
['t[sub ([alpha]/2,N-1)]] is the upper critical value of the Students-t
distribution with /N-1/ degrees of freedom.
[note
The quantity [alpha][space] is the maximum acceptable risk of falsely rejecting
the null-hypothesis. The smaller the value of [alpha] the greater the
strength of the test.
The confidence level of the test is defined as 1 - [alpha], and often expressed
as a percentage. So for example a significance level of 0.05, is equivalent
to a 95% confidence level. Refer to
[@http://www.itl.nist.gov/div898/handbook/prc/section1/prc14.htm
"What are confidence intervals?"] in __handbook for more information.
] [/ Note]
[note
The usual assumptions of
[@http://en.wikipedia.org/wiki/Independent_and_identically-distributed_random_variables independent and identically distributed (i.i.d.)]
variables and [@http://en.wikipedia.org/wiki/Normal_distribution normal distribution]
of course apply here, as they do in other examples.
]
From the formula, it should be clear that:
* The width of the confidence interval decreases as the sample size increases.
* The width increases as the standard deviation increases.
* The width increases as the ['confidence level increases] (0.5 towards 0.99999 - stronger).
* The width increases as the ['significance level decreases] (0.5 towards 0.00000...01 - stronger).
The following example code is taken from the example program
[@../../example/students_t_single_sample.cpp students_t_single_sample.cpp].
We'll begin by defining a procedure to calculate intervals for
various confidence levels; the procedure will print these out
as a table:
// Needed includes:
#include <boost/math/distributions/students_t.hpp>
#include <iostream>
#include <iomanip>
// Bring everything into global namespace for ease of use:
using namespace boost::math;
using namespace std;
void confidence_limits_on_mean(
double Sm, // Sm = Sample Mean.
double Sd, // Sd = Sample Standard Deviation.
unsigned Sn) // Sn = Sample Size.
{
using namespace std;
using namespace boost::math;
// Print out general info:
cout <<
"__________________________________\n"
"2-Sided Confidence Limits For Mean\n"
"__________________________________\n\n";
cout << setprecision(7);
cout << setw(40) << left << "Number of Observations" << "= " << Sn << "\n";
cout << setw(40) << left << "Mean" << "= " << Sm << "\n";
cout << setw(40) << left << "Standard Deviation" << "= " << Sd << "\n";
We'll define a table of significance/risk levels for which we'll compute intervals:
double alpha[] = { 0.5, 0.25, 0.1, 0.05, 0.01, 0.001, 0.0001, 0.00001 };
Note that these are the complements of the confidence/probability levels: 0.5, 0.75, 0.9 .. 0.99999).
Next we'll declare the distribution object we'll need, note that
the /degrees of freedom/ parameter is the sample size less one:
students_t dist(Sn - 1);
Most of what follows in the program is pretty printing, so let's focus
on the calculation of the interval. First we need the t-statistic,
computed using the /quantile/ function and our significance level. Note
that since the significance levels are the complement of the probability,
we have to wrap the arguments in a call to /complement(...)/:
double T = quantile(complement(dist, alpha[i] / 2));
Note that alpha was divided by two, since we'll be calculating
both the upper and lower bounds: had we been interested in a single
sided interval then we would have omitted this step.
Now to complete the picture, we'll get the (one-sided) width of the
interval from the t-statistic
by multiplying by the standard deviation, and dividing by the square
root of the sample size:
double w = T * Sd / sqrt(double(Sn));
The two-sided interval is then the sample mean plus and minus this width.
And apart from some more pretty-printing that completes the procedure.
Let's take a look at some sample output, first using the
[@http://www.itl.nist.gov/div898/handbook/eda/section4/eda428.htm
Heat flow data] from the NIST site. The data set was collected
by Bob Zarr of NIST in January, 1990 from a heat flow meter
calibration and stability analysis.
The corresponding dataplot
output for this test can be found in
[@http://www.itl.nist.gov/div898/handbook/eda/section3/eda352.htm
section 3.5.2] of the __handbook.
[pre'''
__________________________________
2-Sided Confidence Limits For Mean
__________________________________
Number of Observations = 195
Mean = 9.26146
Standard Deviation = 0.02278881
___________________________________________________________________
Confidence T Interval Lower Upper
Value (%) Value Width Limit Limit
___________________________________________________________________
50.000 0.676 1.103e-003 9.26036 9.26256
75.000 1.154 1.883e-003 9.25958 9.26334
90.000 1.653 2.697e-003 9.25876 9.26416
95.000 1.972 3.219e-003 9.25824 9.26468
99.000 2.601 4.245e-003 9.25721 9.26571
99.900 3.341 5.453e-003 9.25601 9.26691
99.990 3.973 6.484e-003 9.25498 9.26794
99.999 4.537 7.404e-003 9.25406 9.26886
''']
As you can see the large sample size (195) and small standard deviation (0.023)
have combined to give very small intervals, indeed we can be
very confident that the true mean is 9.2.
For comparison the next example data output is taken from
['P.K.Hou, O. W. Lau & M.C. Wong, Analyst (1983) vol. 108, p 64.
and from Statistics for Analytical Chemistry, 3rd ed. (1994), pp 54-55
J. C. Miller and J. N. Miller, Ellis Horwood ISBN 0 13 0309907.]
The values result from the determination of mercury by cold-vapour
atomic absorption.
[pre'''
__________________________________
2-Sided Confidence Limits For Mean
__________________________________
Number of Observations = 3
Mean = 37.8000000
Standard Deviation = 0.9643650
___________________________________________________________________
Confidence T Interval Lower Upper
Value (%) Value Width Limit Limit
___________________________________________________________________
50.000 0.816 0.455 37.34539 38.25461
75.000 1.604 0.893 36.90717 38.69283
90.000 2.920 1.626 36.17422 39.42578
95.000 4.303 2.396 35.40438 40.19562
99.000 9.925 5.526 32.27408 43.32592
99.900 31.599 17.594 20.20639 55.39361
99.990 99.992 55.673 -17.87346 93.47346
99.999 316.225 176.067 -138.26683 213.86683
''']
This time the fact that there are only three measurements leads to
much wider intervals, indeed such large intervals that it's hard
to be very confident in the location of the mean.
[endsect]
[section:tut_mean_test Testing a sample mean for difference from a "true" mean]
When calibrating or comparing a scientific instrument or measurement method of some kind,
we want to be answer the question "Does an observed sample mean differ from the
"true" mean in any significant way?". If it does, then we have evidence of
a systematic difference. This question can be answered with a Students-t test:
more information can be found
[@http://www.itl.nist.gov/div898/handbook/eda/section3/eda352.htm
on the NIST site].
Of course, the assignment of "true" to one mean may be quite arbitrary,
often this is simply a "traditional" method of measurement.
The following example code is taken from the example program
[@../../example/students_t_single_sample.cpp students_t_single_sample.cpp].
We'll begin by defining a procedure to determine which of the
possible hypothesis are rejected or not-rejected
at a given significance level:
[note
Non-statisticians might say 'not-rejected' means 'accepted',
(often of the null-hypothesis) implying, wrongly, that there really *IS* no difference,
but statisticans eschew this to avoid implying that there is positive evidence of 'no difference'.
'Not-rejected' here means there is *no evidence* of difference, but there still might well be a difference.
For example, see [@http://en.wikipedia.org/wiki/Argument_from_ignorance argument from ignorance] and
[@http://www.bmj.com/cgi/content/full/311/7003/485 Absence of evidence does not constitute evidence of absence.]
] [/ note]
// Needed includes:
#include <boost/math/distributions/students_t.hpp>
#include <iostream>
#include <iomanip>
// Bring everything into global namespace for ease of use:
using namespace boost::math;
using namespace std;
void single_sample_t_test(double M, double Sm, double Sd, unsigned Sn, double alpha)
{
//
// M = true mean.
// Sm = Sample Mean.
// Sd = Sample Standard Deviation.
// Sn = Sample Size.
// alpha = Significance Level.
Most of the procedure is pretty-printing, so let's just focus on the
calculation, we begin by calculating the t-statistic:
// Difference in means:
double diff = Sm - M;
// Degrees of freedom:
unsigned v = Sn - 1;
// t-statistic:
double t_stat = diff * sqrt(double(Sn)) / Sd;
Finally calculate the probability from the t-statistic. If we're interested
in simply whether there is a difference (either less or greater) or not,
we don't care about the sign of the t-statistic,
and we take the complement of the probability for comparison
to the significance level:
students_t dist(v);
double q = cdf(complement(dist, fabs(t_stat)));
The procedure then prints out the results of the various tests
that can be done, these
can be summarised in the following table:
[table
[[Hypothesis][Test]]
[[The Null-hypothesis: there is
*no difference* in means]
[Reject if complement of CDF for |t| < significance level / 2:
`cdf(complement(dist, fabs(t))) < alpha / 2`]]
[[The Alternative-hypothesis: there
*is difference* in means]
[Reject if complement of CDF for |t| > significance level / 2:
`cdf(complement(dist, fabs(t))) > alpha / 2`]]
[[The Alternative-hypothesis: the sample mean *is less* than
the true mean.]
[Reject if CDF of t > 1 - significance level:
`cdf(complement(dist, t)) < alpha`]]
[[The Alternative-hypothesis: the sample mean *is greater* than
the true mean.]
[Reject if complement of CDF of t < significance level:
`cdf(dist, t) < alpha`]]
]
[note
Notice that the comparisons are against `alpha / 2` for a two-sided test
and against `alpha` for a one-sided test]
Now that we have all the parts in place, let's take a look at some
sample output, first using the
[@http://www.itl.nist.gov/div898/handbook/eda/section4/eda428.htm
Heat flow data] from the NIST site. The data set was collected
by Bob Zarr of NIST in January, 1990 from a heat flow meter
calibration and stability analysis. The corresponding dataplot
output for this test can be found in
[@http://www.itl.nist.gov/div898/handbook/eda/section3/eda352.htm
section 3.5.2] of the __handbook.
[pre
__________________________________
Student t test for a single sample
__________________________________
Number of Observations = 195
Sample Mean = 9.26146
Sample Standard Deviation = 0.02279
Expected True Mean = 5.00000
Sample Mean - Expected Test Mean = 4.26146
Degrees of Freedom = 194
T Statistic = 2611.28380
Probability that difference is due to chance = 0.000e+000
Results for Alternative Hypothesis and alpha = 0.0500
Alternative Hypothesis Conclusion
Mean != 5.000 NOT REJECTED
Mean < 5.000 REJECTED
Mean > 5.000 NOT REJECTED
]
You will note the line that says the probability that the difference is
due to chance is zero. From a philosophical point of view, of course,
the probability can never reach zero. However, in this case the calculated
probability is smaller than the smallest representable double precision number,
hence the appearance of a zero here. Whatever its "true" value is, we know it
must be extraordinarily small, so the alternative hypothesis - that there is
a difference in means - is not rejected.
For comparison the next example data output is taken from
['P.K.Hou, O. W. Lau & M.C. Wong, Analyst (1983) vol. 108, p 64.
and from Statistics for Analytical Chemistry, 3rd ed. (1994), pp 54-55
J. C. Miller and J. N. Miller, Ellis Horwood ISBN 0 13 0309907.]
The values result from the determination of mercury by cold-vapour
atomic absorption.
[pre
__________________________________
Student t test for a single sample
__________________________________
Number of Observations = 3
Sample Mean = 37.80000
Sample Standard Deviation = 0.96437
Expected True Mean = 38.90000
Sample Mean - Expected Test Mean = -1.10000
Degrees of Freedom = 2
T Statistic = -1.97566
Probability that difference is due to chance = 1.869e-001
Results for Alternative Hypothesis and alpha = 0.0500
Alternative Hypothesis Conclusion
Mean != 38.900 REJECTED
Mean < 38.900 NOT REJECTED
Mean > 38.900 NOT REJECTED
]
As you can see the small number of measurements (3) has led to a large uncertainty
in the location of the true mean. So even though there appears to be a difference
between the sample mean and the expected true mean, we conclude that there
is no significant difference, and are unable to reject the null hypothesis.
However, if we were to lower the bar for acceptance down to alpha = 0.1
(a 90% confidence level) we see a different output:
[pre
__________________________________
Student t test for a single sample
__________________________________
Number of Observations = 3
Sample Mean = 37.80000
Sample Standard Deviation = 0.96437
Expected True Mean = 38.90000
Sample Mean - Expected Test Mean = -1.10000
Degrees of Freedom = 2
T Statistic = -1.97566
Probability that difference is due to chance = 1.869e-001
Results for Alternative Hypothesis and alpha = 0.1000
Alternative Hypothesis Conclusion
Mean != 38.900 REJECTED
Mean < 38.900 NOT REJECTED
Mean > 38.900 REJECTED
]
In this case, we really have a borderline result,
and more data (and/or more accurate data),
is needed for a more convincing conclusion.
[endsect]
[section:tut_mean_size Estimating how large a sample size would have to become
in order to give a significant Students-t test result with a single sample test]
Imagine you have conducted a Students-t test on a single sample in order
to check for systematic errors in your measurements. Imagine that the
result is borderline. At this point one might go off and collect more data,
but it might be prudent to first ask the question "How much more?".
The parameter estimators of the students_t_distribution class
can provide this information.
This section is based on the example code in
[@../../example/students_t_single_sample.cpp students_t_single_sample.cpp]
and we begin by defining a procedure that will print out a table of
estimated sample sizes for various confidence levels:
// Needed includes:
#include <boost/math/distributions/students_t.hpp>
#include <iostream>
#include <iomanip>
// Bring everything into global namespace for ease of use:
using namespace boost::math;
using namespace std;
void single_sample_find_df(
double M, // M = true mean.
double Sm, // Sm = Sample Mean.
double Sd) // Sd = Sample Standard Deviation.
{
Next we define a table of significance levels:
double alpha[] = { 0.5, 0.25, 0.1, 0.05, 0.01, 0.001, 0.0001, 0.00001 };
Printing out the table of sample sizes required for various confidence levels
begins with the table header:
cout << "\n\n"
"_______________________________________________________________\n"
"Confidence Estimated Estimated\n"
" Value (%) Sample Size Sample Size\n"
" (one sided test) (two sided test)\n"
"_______________________________________________________________\n";
And now the important part: the sample sizes required. Class
`students_t_distribution` has a static member function
`find_degrees_of_freedom` that will calculate how large
a sample size needs to be in order to give a definitive result.
The first argument is the difference between the means that you
wish to be able to detect, here it's the absolute value of the
difference between the sample mean, and the true mean.
Then come two probability values: alpha and beta. Alpha is the
maximum acceptable risk of rejecting the null-hypothesis when it is
in fact true. Beta is the maximum acceptable risk of failing to reject
the null-hypothesis when in fact it is false.
Also note that for a two-sided test, alpha must be divided by 2.
The final parameter of the function is the standard deviation of the sample.
In this example, we assume that alpha and beta are the same, and call
`find_degrees_of_freedom` twice: once with alpha for a one-sided test,
and once with alpha/2 for a two-sided test.
for(unsigned i = 0; i < sizeof(alpha)/sizeof(alpha[0]); ++i)
{
// Confidence value:
cout << fixed << setprecision(3) << setw(10) << right << 100 * (1-alpha[i]);
// calculate df for single sided test:
double df = students_t::find_degrees_of_freedom(
fabs(M - Sm), alpha[i], alpha[i], Sd);
// convert to sample size:
double size = ceil(df) + 1;
// Print size:
cout << fixed << setprecision(0) << setw(16) << right << size;
// calculate df for two sided test:
df = students_t::find_degrees_of_freedom(
fabs(M - Sm), alpha[i]/2, alpha[i], Sd);
// convert to sample size:
size = ceil(df) + 1;
// Print size:
cout << fixed << setprecision(0) << setw(16) << right << size << endl;
}
cout << endl;
}
Let's now look at some sample output using data taken from
['P.K.Hou, O. W. Lau & M.C. Wong, Analyst (1983) vol. 108, p 64.
and from Statistics for Analytical Chemistry, 3rd ed. (1994), pp 54-55
J. C. Miller and J. N. Miller, Ellis Horwood ISBN 0 13 0309907.]
The values result from the determination of mercury by cold-vapour
atomic absorption.
Only three measurements were made, and the Students-t test above
gave a borderline result, so this example
will show us how many samples would need to be collected:
[pre'''
_____________________________________________________________
Estimated sample sizes required for various confidence levels
_____________________________________________________________
True Mean = 38.90000
Sample Mean = 37.80000
Sample Standard Deviation = 0.96437
_______________________________________________________________
Confidence Estimated Estimated
Value (%) Sample Size Sample Size
(one sided test) (two sided test)
_______________________________________________________________
75.000 3 4
90.000 7 9
95.000 11 13
99.000 20 22
99.900 35 37
99.990 50 53
99.999 66 68
''']
So in this case, many more measurements would have had to be made,
for example at the 95% level, 14 measurements in total for a two-sided test.
[endsect]
[section:two_sample_students_t Comparing the means of two samples with the Students-t test]
Imagine that we have two samples, and we wish to determine whether
their means are different or not. This situation often arises when
determining whether a new process or treatment is better than an old one.
In this example, we'll be using the
[@http://www.itl.nist.gov/div898/handbook/eda/section3/eda3531.htm
Car Mileage sample data] from the
[@http://www.itl.nist.gov NIST website]. The data compares
miles per gallon of US cars with miles per gallon of Japanese cars.
The sample code is in
[@../../example/students_t_two_samples.cpp students_t_two_samples.cpp].
There are two ways in which this test can be conducted: we can assume
that the true standard deviations of the two samples are equal or not.
If the standard deviations are assumed to be equal, then the calculation
of the t-statistic is greatly simplified, so we'll examine that case first.
In real life we should verify whether this assumption is valid with a
Chi-Squared test for equal variances.
We begin by defining a procedure that will conduct our test assuming equal
variances:
// Needed headers:
#include <boost/math/distributions/students_t.hpp>
#include <iostream>
#include <iomanip>
// Simplify usage:
using namespace boost::math;
using namespace std;
void two_samples_t_test_equal_sd(
double Sm1, // Sm1 = Sample 1 Mean.
double Sd1, // Sd1 = Sample 1 Standard Deviation.
unsigned Sn1, // Sn1 = Sample 1 Size.
double Sm2, // Sm2 = Sample 2 Mean.
double Sd2, // Sd2 = Sample 2 Standard Deviation.
unsigned Sn2, // Sn2 = Sample 2 Size.
double alpha) // alpha = Significance Level.
{
Our procedure will begin by calculating the t-statistic, assuming
equal variances the needed formulae are:
[equation dist_tutorial1]
where Sp is the "pooled" standard deviation of the two samples,
and /v/ is the number of degrees of freedom of the two combined
samples. We can now write the code to calculate the t-statistic:
// Degrees of freedom:
double v = Sn1 + Sn2 - 2;
cout << setw(55) << left << "Degrees of Freedom" << "= " << v << "\n";
// Pooled variance:
double sp = sqrt(((Sn1-1) * Sd1 * Sd1 + (Sn2-1) * Sd2 * Sd2) / v);
cout << setw(55) << left << "Pooled Standard Deviation" << "= " << sp << "\n";
// t-statistic:
double t_stat = (Sm1 - Sm2) / (sp * sqrt(1.0 / Sn1 + 1.0 / Sn2));
cout << setw(55) << left << "T Statistic" << "= " << t_stat << "\n";
The next step is to define our distribution object, and calculate the
complement of the probability:
students_t dist(v);
double q = cdf(complement(dist, fabs(t_stat)));
cout << setw(55) << left << "Probability that difference is due to chance" << "= "
<< setprecision(3) << scientific << 2 * q << "\n\n";
Here we've used the absolute value of the t-statistic, because we initially
want to know simply whether there is a difference or not (a two-sided test).
However, we can also test whether the mean of the second sample is greater
or is less (one-sided test) than that of the first:
all the possible tests are summed up in the following table:
[table
[[Hypothesis][Test]]
[[The Null-hypothesis: there is
*no difference* in means]
[Reject if complement of CDF for |t| < significance level / 2:
`cdf(complement(dist, fabs(t))) < alpha / 2`]]
[[The Alternative-hypothesis: there is a
*difference* in means]
[Reject if complement of CDF for |t| > significance level / 2:
`cdf(complement(dist, fabs(t))) < alpha / 2`]]
[[The Alternative-hypothesis: Sample 1 Mean is *less* than
Sample 2 Mean.]
[Reject if CDF of t > significance level:
`cdf(dist, t) > alpha`]]
[[The Alternative-hypothesis: Sample 1 Mean is *greater* than
Sample 2 Mean.]
[Reject if complement of CDF of t > significance level:
`cdf(complement(dist, t)) > alpha`]]
]
[note
For a two-sided test we must compare against alpha / 2 and not alpha.]
Most of the rest of the sample program is pretty-printing, so we'll
skip over that, and take a look at the sample output for alpha=0.05
(a 95% probability level). For comparison the dataplot output
for the same data is in
[@http://www.itl.nist.gov/div898/handbook/eda/section3/eda353.htm
section 1.3.5.3] of the __handbook.
[pre'''
________________________________________________
Student t test for two samples (equal variances)
________________________________________________
Number of Observations (Sample 1) = 249
Sample 1 Mean = 20.145
Sample 1 Standard Deviation = 6.4147
Number of Observations (Sample 2) = 79
Sample 2 Mean = 30.481
Sample 2 Standard Deviation = 6.1077
Degrees of Freedom = 326
Pooled Standard Deviation = 6.3426
T Statistic = -12.621
Probability that difference is due to chance = 5.273e-030
Results for Alternative Hypothesis and alpha = 0.0500'''
Alternative Hypothesis Conclusion
Sample 1 Mean != Sample 2 Mean NOT REJECTED
Sample 1 Mean < Sample 2 Mean NOT REJECTED
Sample 1 Mean > Sample 2 Mean REJECTED
]
So with a probability that the difference is due to chance of just
5.273e-030, we can safely conclude that there is indeed a difference.
The tests on the alternative hypothesis show that we must
also reject the hypothesis that Sample 1 Mean is
greater than that for Sample 2: in this case Sample 1 represents the
miles per gallon for Japanese cars, and Sample 2 the miles per gallon for
US cars, so we conclude that Japanese cars are on average more
fuel efficient.
Now that we have the simple case out of the way, let's look for a moment
at the more complex one: that the standard deviations of the two samples
are not equal. In this case the formula for the t-statistic becomes:
[equation dist_tutorial2]
And for the combined degrees of freedom we use the
[@http://en.wikipedia.org/wiki/Welch-Satterthwaite_equation Welch-Satterthwaite]
approximation:
[equation dist_tutorial3]
Note that this is one of the rare situations where the degrees-of-freedom
parameter to the Student's t distribution is a real number, and not an
integer value.
[note
Some statistical packages truncate the effective degrees of freedom to
an integer value: this may be necessary if you are relying on lookup tables,
but since our code fully supports non-integer degrees of freedom there is no
need to truncate in this case. Also note that when the degrees of freedom
is small then the Welch-Satterthwaite approximation may be a significant
source of error.]
Putting these formulae into code we get:
// Degrees of freedom:
double v = Sd1 * Sd1 / Sn1 + Sd2 * Sd2 / Sn2;
v *= v;
double t1 = Sd1 * Sd1 / Sn1;
t1 *= t1;
t1 /= (Sn1 - 1);
double t2 = Sd2 * Sd2 / Sn2;
t2 *= t2;
t2 /= (Sn2 - 1);
v /= (t1 + t2);
cout << setw(55) << left << "Degrees of Freedom" << "= " << v << "\n";
// t-statistic:
double t_stat = (Sm1 - Sm2) / sqrt(Sd1 * Sd1 / Sn1 + Sd2 * Sd2 / Sn2);
cout << setw(55) << left << "T Statistic" << "= " << t_stat << "\n";
Thereafter the code and the tests are performed the same as before. Using
are car mileage data again, here's what the output looks like:
[pre'''
__________________________________________________
Student t test for two samples (unequal variances)
__________________________________________________
Number of Observations (Sample 1) = 249
Sample 1 Mean = 20.145
Sample 1 Standard Deviation = 6.4147
Number of Observations (Sample 2) = 79
Sample 2 Mean = 30.481
Sample 2 Standard Deviation = 6.1077
Degrees of Freedom = 136.87
T Statistic = -12.946
Probability that difference is due to chance = 1.571e-025
Results for Alternative Hypothesis and alpha = 0.0500'''
Alternative Hypothesis Conclusion
Sample 1 Mean != Sample 2 Mean NOT REJECTED
Sample 1 Mean < Sample 2 Mean NOT REJECTED
Sample 1 Mean > Sample 2 Mean REJECTED
]
This time allowing the variances in the two samples to differ has yielded
a higher likelihood that the observed difference is down to chance alone
(1.571e-025 compared to 5.273e-030 when equal variances were assumed).
However, the conclusion remains the same: US cars are less fuel efficient
than Japanese models.
[endsect]
[section:paired_st Comparing two paired samples with the Student's t distribution]
Imagine that we have a before and after reading for each item in the sample:
for example we might have measured blood pressure before and after administration
of a new drug. We can't pool the results and compare the means before and after
the change, because each patient will have a different baseline reading.
Instead we calculate the difference between before and after measurements
in each patient, and calculate the mean and standard deviation of the differences.
To test whether a significant change has taken place, we can then test
the null-hypothesis that the true mean is zero using the same procedure
we used in the single sample cases previously discussed.
That means we can:
* [link math_toolkit.stat_tut.weg.st_eg.tut_mean_intervals Calculate confidence intervals of the mean].
If the endpoints of the interval differ in sign then we are unable to reject
the null-hypothesis that there is no change.
* [link math_toolkit.stat_tut.weg.st_eg.tut_mean_test Test whether the true mean is zero]. If the
result is consistent with a true mean of zero, then we are unable to reject the
null-hypothesis that there is no change.
* [link math_toolkit.stat_tut.weg.st_eg.tut_mean_size Calculate how many pairs of readings we would need
in order to obtain a significant result].
[endsect]
[endsect][/section:st_eg Student's t]
[/
Copyright 2006, 2012 John Maddock and Paul A. Bristow.
Distributed under the Boost Software License, Version 1.0.
(See accompanying file LICENSE_1_0.txt or copy at
http://www.boost.org/LICENSE_1_0.txt).
]
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