The Case for Torture It is generally assumed that torture is impermissible, a throwback to a more brutal age. Enlightened societies reject it outright, and regimes suspected of using it risk the wrath of the United States. I believe this attitude is unwise. There are situations in which torture is not merely permissible but morally mandatory. Moreover, these situations are moving from the realm of imagination to fact. Death: Suppose a terrorist has hidden an atomic bomb on Manhattan Island which will detonate at noon on July 4 unless . . . . ( here follow the usual demands for money and release of his friends from jail). Suppose, further, that he is caught at 10 A.M. on that fateful day but—preferring death to failure—won’t disclose where the bomb is. What do we do? If we follow due process—wait for his lawyer and arraign him—millions of people will die. If the only way to save those lives is to subject the terrorist to the most excruciating possible pain, what grounds can their be for not doing so? I suggest there are none. In any case, I ask you to face the question with an open mind. Torturing the terrorist is unconstitutional? Probably. But millions of lives surely outweigh constitutionality. Torture is barbaric? Mass murder is far more barbaric. Indeed letting millions die in deference to one who flaunts his guilt is moral cowardice, an unwillingness to dirty one’s hands. If you caught the terrorist, could you sleep nights knowing that millions died because you could not bring yourself to apply the electrodes? Once you concede that torture is justified in extreme cases, you have admitted that the decision to use torture is a matter of balancing innocent lives against the means to save them. You must now face more realistic cases involving more modest numbers. Someone plants a bomb on a jumbo jet. He alone can disarm it, and his demands cannot be met (or if they can, we refuse to set a precedent by yielding to his threats). Surely we can, we must, do anything to the extortionist to save the passengers. How can we tell 300, or 100, or 10 people who never asked to be put in danger, “I’m sorry, you’ll have to die in agony, we just couldn’t bring ourselves to. . . . “ Here are the results of an informal poll about a third, hypothetical case. Suppose a terrorist group kidnapped a newborn baby from a hospital. I asked four mothers if they would approve of torturing kidnappers if that were necessary to get their own newborns back. All said “yes”, the most “liberal” adding that she would like to administer it herself. I am not advocating torture as a punishment. Punishment is addressed to deeds irrevocably past. Rather, I am advocating torture as an acceptable measure for preventing future evils. So understood, it is far less objectionable than many extant punishments. Opponents of the death penalty, for example, are forever insisting that executing a murderer will not bring back the victim (as if the purpose ...

1. The Case for Torture
It is generally assumed that torture is impermissible, a
throwback to a more brutal age. Enlightened societies reject it
outright, and regimes suspected of using it risk the wrath of the
United States.
I believe this attitude is unwise. There are situations in which
torture is not merely permissible but morally mandatory.
Moreover, these situations are moving from the realm of
imagination to fact.
Death: Suppose a terrorist has hidden an atomic bomb on
Manhattan Island which will detonate at noon on July 4 unless .
. . . ( here follow the usual demands for money and release of
his friends from jail). Suppose, further, that he is caught at 10
A.M. on that fateful day but—preferring death to failure—won’t
disclose where the bomb is. What do we do? If we follow due
process—wait for his lawyer and arraign him—millions of
people will die. If the only way to save those lives is to subject
the terrorist to the most excruciating possible pain, what
grounds can their be for not doing so? I suggest there are none.
In any case, I ask you to face the question with an open mind.
Torturing the terrorist is unconstitutional? Probably. But
millions of lives surely outweigh constitutionality. Torture is
barbaric? Mass murder is far more barbaric. Indeed letting
millions die in deference to one who flaunts his guilt is moral
cowardice, an unwillingness to dirty one’s hands. If you caught
the terrorist, could you sleep nights knowing that millions died
because you could not bring yourself to apply the electrodes?
Once you concede that torture is justified in extreme cases, you

2. have admitted that the decision to use torture is a matter of
balancing innocent lives against the means to save them. You
must now face more realistic cases involving more modest
numbers. Someone plants a bomb on a jumbo jet. He alone can
disarm it, and his demands cannot be met (or if they can, we
refuse to set a precedent by yielding to his threats). Surely we
can, we must, do anything to the extortionist to save the
passengers. How can we tell 300, or 100, or 10 people who
never asked to be put in danger, “I’m sorry, you’ll have to die
in agony, we just couldn’t bring ourselves to. . . . “
Here are the results of an informal poll about a third,
hypothetical case. Suppose a terrorist group kidnapped a
newborn baby from a hospital. I asked four mothers if they
would approve of torturing kidnappers if that were necessary to
get their own newborns back. All said “yes”, the most “liberal”
adding that she would like to administer it herself.
I am not advocating torture as a punishment. Punishment is
addressed to deeds irrevocably past. Rather, I am advocating
torture as an acceptable measure for preventing future evils. So
understood, it is far less objectionable than many extant
punishments. Opponents of the death penalty, for example, are
forever insisting that executing a murderer will not bring back
the victim (as if the purpose of capital punishment were
resurrection, not deterrence or retribution). But torture, in the
cases described, is intended not to bring anyone back but to
keep innocents from being dispatched. The most powerful
argument against using torture as a punishment or to secure
confessions is that such practices disregard the rights of the
individual. Well, if the individual is all that important—and he
is—it is correspondingly important to protect the rights of
individuals threatened by terrorists. If life is so valuable that it
must never be taken, the lives of the innocents must be saved

3. even at the price of hurting the one who endangers them.
Better precedents for torture are assassination and pre-emptive
attack. No Allied leader would have flinched at assassinating
Hitler, had that been possible. (The Allies did assassinate
Heydrich.) Americans will be angered to learn that Roosevelt
could have had Hitler killed in 1943—thereby shortening the
war and saving millions of lives—but refused on moral grounds.
Similarly, if nation A learns that nation B is about to launch an
unprovoked attack, A has the right to save itself by destroying
B’s military capability first. In the same way, if police can by
torture save those who would otherwise die at the hands of
kidnappers or terrorists, they must.
Idealism: There is an important difference between terrorists
and their victims that should mute talk of the terrorist’s
“rights”. The terrorist’s victims are at risk unintentionally, not
having asked to be endangered. But the terrorist knowingly
initiated his actions. Unlike his victims, he volunteered for the
risks of his deed. By threatening to kill for profit or idealism,
he renounces civilized standards, and he can have no complaint
if civilization tries to thwart him by whatever means necessary.
Just as torture is justified only to save lives (not to extort
confessions or recantations) it is justifiably administered only
to those known to hold innocent lives in their hands. Ah, but
how can authorities ever be sure they have the right malefactor?
Isn’t there a danger of error and abuse? Won’t WE turn into
THEM?
Questions like that are disingenuous in a world in which
terrorists proclaim themselves and perform for television. The
name of their game is public recognition. After all, you can’t

4. very well intimidate a government into releasing your freedom
fighters unless you announce that it is your group that has
seized the embassy. “Clear guilt” is difficult to define, but
when 40 million people see a group of masked gunmen seize an
airplane on the evening news, there is not much question who
the perpetrators are. There will be hard cases where the
situation is murkier. Nevertheless, a line demarcating the
legitimate use of torture can be drawn. Torture only the
obviously guilty, and only for the sake of saving innocents and
the line between US and THEM will remain clear.
There is little danger that the Western democracies will lose
their way if they choose to inflict pain as a way of preserving
order. Paralysis in the face of evil is a greater danger.
Someday soon a terrorist will threaten tens of thousands of
loves and torture will be the only way to save them. We had
better start thinking about this.
Michael Levin, educated at Michigan State University and
Columbia University, has taught philosophy at Columbia and
now at City College of the City University of New York. Levin
has written numerous papers for professional journals and a
book entitled, metaphysics and the Mind-Body Problem. His
most recent book (with Lawrence Thomas) is Sexual Orientation
and Human Rights (1999). The preceding essay is intended for
a general audience.
DataIDSalCompaMidAgeEESSERGRaiseDegGen1Gr1581.01757
3485805.70METhe ongoing question that the weekly
assignments will focus on is: Are males and females paid the
same for equal work (under the Equal Pay
Act)?2270.870315280703.90MBNote: to simplfy the analysis,
we will assume that jobs within each grade comprise equal
work.3341.096313075513.61FB4661.15757421001605.51METh

5. e column labels in the table
mean:5470.9794836901605.71MDID – Employee sample
numberSal – Salary in thousands6761.1346736701204.51MFAge
– Age in yearsEES – Appraisal rating (Employee evaluation
score)7411.0254032100815.71FCSER – Years of serviceG –
Gender (0 = male, 1 = female)8231.000233290915.81FAMid –
salary grade midpointRaise – percent of last
raise9771.149674910010041MFGrade – job/pay gradeDeg (0=
BSBA 1 = MS)10220.956233080714.71FAGen1 (Male or
Female)Compa - salary divided by midpoint, a measure of
salary that removes the impact of
grade11231.00023411001914.81FA12601.0525752952204.50M
EThis data should be treated as a sample of employees taken
from a company that has about
1,00013421.0504030100214.70FCemployees using a random
sampling
approach.14241.04323329012161FA15241.043233280814.91FA
16471.175404490405.70MCMac Users: The homework in this
course assumes students have Windows Excel,
and17691.2105727553131FEcan load the Analysis ToolPak into
their version of Excel.18361.1613131801115.60FBThe analysis
tool pak has been removed from Excel for Windows, but a free
third-party19241.043233285104.61MAtool that can be used
(found on an answers Microsoft site)
is:20341.0963144701614.80FBhttp://www.analystsoft.com/en/pr
oducts/statplusmacle21761.1346743951306.31MFLike the
Microsoft site, I make cannot guarantee the program, but do
know that22571.187484865613.81FDStatplus is a respected
statistical package.You may use other approaches or
tools23231.000233665613.30FAas desired to complete the
assignments.24501.041483075913.80FD25241.0432341704040
MA26241.043232295216.20FA27401.000403580703.91MC2875
1.119674495914.40FF29721.074675295505.40MF30491.020484
5901804.30MD31241.043232960413.91FA32280.903312595405
.60MB33641.122573590905.51ME34280.903312680204.91MB3
5241.043232390415.30FA36231.000232775314.30FA37220.956

6. 232295216.20FA38560.9825745951104.50ME39351.129312790
615.50FB40251.086232490206.30MA41431.075402580504.30M
C42241.0432332100815.71FA43771.1496742952015.50FF4460
1.0525745901605.21ME45551.145483695815.21FD46651.1405
739752003.91ME47621.087573795505.51ME48651.1405734901
115.31FE49601.0525741952106.60ME50661.1575738801204.60
ME
http://www.analystsoft.com/en/products/statplusmacle
Week 1Week 1.Describing the data.<Use right click on the row
numbers at the left to insert rows below each question for your
results and comments.>1Using the Excel Analysis ToolPak
function descriptive statistics, generate and show the
descriptive statistics for each appropriate variable in the sample
data set.a. For which variables in the data set does this function
not work correctly for? Why?2Sort the data by Gen or Gen 1
(into males and females) and find the mean and standard
deviation for each gender for the following variables:sal,
compa, age, sr and raise.Use either the descriptive stats function
or the Fx functions (average and stdev).3What is the probability
for a:a. Randomly selected person being a male in grade
E?b. Randomly selected male being in grade E?c. Why are
the results different?4Find:a.The z score for each male salary,
based on only the male salaries.b.The z score for each female
salary, based on only the female salaries.c.The z score for each
female compa, based on only the female compa values.d.The z
score for each male compa, based on only the male compa
values.e.What do the distributions and spread suggest about
male and female salaries?Why might we want to use compa to
measure salaries between males and females?5Based on this
sample, what conclusions can you make about the issue of male
and female pay equality?Are all of the results consistent with
your conclusion? If not, why not?
Week 2 Week 2Testing means with the t-test<Note: use right
click on row numbers to insert rows to perform analysis below
any question>For questions 2 and 3 below, be sure to list the
null and alternate hypothesis statements. Use .05 for your

7. significance level in making your decisions.For full credit, you
need to also show the statistical outcomes - either the Excel test
result or the calculations you performed.1Below are 2 one-
sample t-tests comparing male and female average salaries to
the overall sample mean.Based on our sample, how do you
interpret the results and what do these results suggest about the
population means for male and female
salaries?MalesFemalesHo: Mean salary = 45Ho: Mean salary =
45Ha: Mean salary =/= 45Ha: Mean salary =/= 45Note when
performing a one sample test with ANOVA, the second variable
(Ho) is listed as the same value for every corresponding value
in the data set.t-Test: Two-Sample Assuming Unequal
Variancest-Test: Two-Sample Assuming Unequal
VariancesSince the Ho variable has Var = 0, variances are
unequal; this test defaults to 1 sample t in this
situationMaleHoFemaleHoMean5245Mean3845Variance3160Va
riance334.66666666670Observations2525Observations2525Hyp
othesized Mean Difference0Hypothesized Mean
Difference0df24df24t Stat1.9689038266t Stat-
1.9132063573P(T<=t) one-tail0.0303078503P(T<=t) one-
tail0.0338621184t Critical one-tail1.7108820799t Critical one-
tail1.7108820799P(T<=t) two-tail0.0606157006P(T<=t) two-
tail0.0677242369t Critical two-tail2.0638985616t Critical two-
tail2.0638985616Conclusion: Do not reject Ho; mean equals
45Conclusion: Do not reject Ho; mean equals
45Interpretation:2Based on our sample results, perform a 2-
sample t-test to see if the population male and female salaries
could be equal to each other.3Based on our sample results, can
the male and female compas in the population be equal to each
other? (Another 2-sample t-test.)4What other information would
you like to know to answer the question about salary equity
between the genders? Why?5If the salary and compa mean tests
in questions 3 and 4 provide different results about male and
female salary equality,which would be more appropriate to use
in answering the question about salary equity? Why?What are
your conclusions about equal pay at this point?

8. Week 3Week 3Testing multiple means with ANOVA<Note: use
right click on row numbers to insert rows to perform analysis
below any question>For questions 3 and 4 below, be sure to list
the null and alternate hypothesis statements. Use .05 for your
significance level in making your decisions.For full credit, you
need to also show the statistical outcomes - either the Excel test
result or the calculations you performed.1. Based on the
sample data, can the average(mean) salary in the population be
the same for each of the grade levels? (Assume equal variance,
and use the analysis toolpak function ANOVA.)Set up the input
table/range to use as follows: Put all of the salary values for
each grade under the appropriate grade label.Be sure to incllude
the null and alternate hypothesis along with the statistical test
and result.ABCDEFNote: Assume equal variances for all
grades.2. The table and analysis below demonstrate a 2-way
ANOVA with replication. Please interpret the
results.GradeGenderABCDEFM242740475676The salary values
were randomly picked for each
cell.252847496677F223441506575243642576977Ho: Average
salaries are equal for all gradesHa: Average salaries are not
equal for all gradesHo: Average salaries by gender are equalHa:
Average salaries by gender are not equalHo: Interaction is not
significantHa: Interaction is significantPerform analysis:Anova:
Two-Factor With
ReplicationSUMMARYABCDEFTotalMCount22222212Sum495
58796122153562Average24.527.543.5486176.546.8333333333V
ariance0.50.524.52500.5364.5151515152FCount22222212Sum4
67083107134152592Average233541.553.5677649.3333333333V
ariance220.524.582367.3333333333TotalCount444444Sum9512
5170203256305Average23.7531.2542.550.756476.25Variance1.
583333333319.58333333339.666666666718.916666666731.333
33333330.9166666667ANOVASource of VariationSSdfMSFP-
valueF
critSample37.5137.53.84615384620.07348333714.7472253467C
olumns7841.833333333351568.3666666667160.85811965810.0
0000000013.1058752391Note: a number with an E after it (E9

9. or E-6, for
example)Interaction91.5518.31.87692307690.17230826083.105
8752391means we move the decimal point that number of
places.Within117129.75For example, 1.2E4 becomes 12000;
while 4.56E-5 becomes 0.0000456Total8087.833333333323Do
we reject or not reject each of the null hypotheses? What do
your conclusions mean about the population values being
tested?Interpretation:3. Using our sample results, can we say
that the compa values in the population are equal by grade
and/or gender, and are independent of each factor?GradeBe sure
to include the null and alternate hypothesis along with the
statistical test and result.GenderABCDEF<Randomly pick
compas to fill each cell - for exampe, a compaMfor the
intersection of M and A might be 1.043.><If desired, you can
use the compa values that relate to theFsalary values used in
question 2 for a more direct comparison of the
twooutcomes.>Conduct and show the results of a 2-way
ANOVA with replication using the completed table above. The
results should look something like those in question 2.Interpret
the results. Are the average compas for each gender (listed as
sample) equal? For each grade? Do grade and gender
interaction impact compa values?4. Pick any other variable you
are interested in and do a simple 2-way ANOVA without
replication. Why did you pick this variable and what do the
results show?Variable name:Be sure to include the null and
alternate hypothesis along with the statistical test and
result.GenderABCDEFMHint: use mean values in the
boxes.F5. Using the results for this week, What are your
conclusions about gender equal pay for equal work at this
point?
Week 4Week 4Confidence Intervals and Chi Square (Chs 11 -
12)Let's look at some other factors that might influence
pay.Q1Q2<Note: use right click on row numbers to insert rows
to perform analysis below any question>For question 3 below,
be sure to list the null and alternate hypothesis statements. Use
.05 for your significance level in making your

10. decisions.GrDegGen1SalFor full credit, you need to also show
the statistical outcomes - either the Excel test result or the
calculations you performed.A0F341One question we might have
is if the distribution of graduate and undergraduate degrees
independent of the grade the employee?A0F41(Note: this is the
same as asking if the degrees are distributed the same
way.)Based on the analysis of our sample data (shown below),
what is your answer?Ho: The populaton correlation between
grade and degree is 0.C0F77Ha: The population correlation
between grade and degree is > 0Perform
analysis:OBSERVEDABCDEFTotalCOUNT - M or
075325325COUNT - F or
182237325total1575512650EXPECTED7.53.52.52.56325<Highl
ighting each cell with show how the value7.53.52.52.56325is
found: row total times column total divided by1575512650grand
total.>By using either the Excel Chi Square functions or
calculating the results directly as the text shows, do wereject or
not reject the null hypothesis? What does your conclusion
mean?Interpretation:2Using our sample data, we can construct a
95% confidence interval for the population's mean salary for
each gender.Interpret the results. How do they compare with
the findings in the week 2 one sample t-test outcomes (Question
1)?MalesMeanSt
errorLowtoHigh523.658779395744.448279327259.5517206728
Results are mean +/-2.064*standard
errorFemales383.622754176930.522635378945.47736462112.06
4 is t value for 95% interval<Reminder: standard error is the
sample standard deviation divided by the square root of the
sample size.>Interpretation:C0F55D1M773Based on our sample
data, can we conclude that males and females are distributed
across grades in a similar pattern within the
population?D1M604Using our sample data, construct a 95%
confidence interval for the population's mean salary difference
for each gender.Do they intersect or overlap? How do these
results compare to the findings in week 2, question 2?5How do
you interpret these results in light of our question about equal

11. pay for equal work?
Week 5Week 5 Correlation and RegressionFor each question
involving a statistical test below, list the null and alternate
hypothesis statements. Use .05 for your significance level in
making your decisions.For full credit, you need to also show the
statistical outcomes - either the Excel test result or the
calculations you performed.1Create a correlation table for the
variables in our data set. (Use analysis ToolPak function
Correlation.)a. Interpret the results. What variables seem to be
important in seeing if we pay males and females equally for
equal work?2Below is a regression analysis for salary being
predicted/explained by the other variables in our sample
(Mid,age, ees, sr, raise, and deg variables.) (Note: since salary
and compa are different ways ofexpressing an employee’s
salary, we do not want to have both used in the same
regression.)Ho: The regression equation is not significant.Ha:
The regression equation is significant.Ho: The regression
coefficient for each variable is not significantHa: The
regression coefficient for each variable is significantSalThe
analysis used Sal as the y (dependent variable) andSUMMARY
OUTPUTmid, age, ees, sr, g, raise, and deg as the
dependentvariables (entered as a range).Regression
StatisticsMultiple R0.9921549762R
Square0.9843714969Adjusted R Square0.9817667464Standard
Error2.5927763074Observations50ANOVAdfSSMSFSignificanc
e
FRegression717783.65546282842540.5222089755377.91392688
488.44042689148567E-
36Residual42282.34453717166.7224889803Total4918066Coeffi
cientsStandard Errort StatP-valueLower 95%Upper 95%Lower
95.0%Upper 95.0%Intercept-4.0093.775-1.0620.294-
11.6273.609-
11.6273.609Mid1.2200.03040.6740.0001.1591.2801.1591.280A
ge0.0290.0670.4390.663-0.1050.164-0.1050.164EES-
0.0960.047-2.0200.050-0.191-0.000-0.191-0.000SR-0.0740.084-
0.8760.386-0.2440.096-

12. 0.2440.096G2.5520.8473.0120.0040.8424.2610.8424.261Raise0.
8340.6431.2990.201-0.4622.131-
0.4622.131Deg1.0020.7441.3470.185-0.5002.504-
0.5002.504Interpretation:Do you reject or not reject the
regression null hypothesis?Do you reject or not reject the null
hypothesis for each variable?What is the regression equation,
using only significant variables if any exist?What does result
tell us about equal pay for equal work for males and
females?3Perform a regression analysis using compa as the
dependent variable and the same independentvariables as used
in question 2. Show the result, and interpret your findings by
answering the same questions.Note: be sure to include the
appropriate hypothesis statements.4Based on all of your results
to date, is gender a factor in the pay practices of this company?
Why or why not?Which is the best variable to use in analyzing
pay practices - salary or compa? Why?5Why did the single
factor tests and analysis (such as t and single factor ANOVA
tests on salary equality) not provide a complete answer to our
salary equality question?What outcomes in your life or work
might benefit from a multiple regression examination rather
than a simpler one variable test?