INDUCTIVE REASONING IN

LAW
• Unlike deductive arguments which draw out truth or
information already contained in the premises, inductive
arguments give us truth or information more than what the
premises are saying.
• In an inductive argument, what is claimed in the conclusion
goes beyond the evidence found in the premises. It is for this
reason that inductive arguments do not claim that their
conclusion is certain or that their premises guarantee the
truth of the conclusion. What inductive arguments claim is
that their conclusion, based on the premises, is likely or
probably true.
• The absence of complete certainty, however, does not
dilute the importance of induction in the law.
Deductive reasoning is not applicable in cases where
there is no established law, or binding precedent, or
clear statute to provide the major premise of our legal
argument.
 Inductive Generalization
• There are many types of inductive reasoning, and the
simplest and most common of these types is called
"inductive generalization.“
• An inductive generalization is "an argument that relies on
characteristics of a sample population to make a claim about
the population as a whole. "This claim is a general claim that
makes a statement about all, most, or some members of a
class, group, or set.
 The following are some examples of general
claims:
• All law students are required to study taxation.
• Every performance-enhancing drug is banned in the
Tour de France.
• Hearsays are not admissible in courts.
• Most congressmen are against the legalization of
divorce.
• Although the style of each of these four claims differ, all are
general claims. Since the first two use the words "all" and
"every", we can recognize without much trouble that they
refer to all members of a class, group, or set. The first refers
to all members of the group law students; the second refers
to all members of the group performance- enhancing drugs.
Even though the third claim does not use the words every or
all, it too is general claim. Further, this claim is no less
general just because it tells us what hearsays are not rather
than what they are. The final example specifically mentions
most congressmen, but it should be understood to refer to
the entire class of congressmen. It makes a general claim
about the whole class of congressmen because it claims that
most are against the legalization of divorce and implies that
the remainder are not against it.
• An inductive generalization uses evidence about a
limited number of people or things of a certain type
(the sample population), to make a general claim
about a larger group of people or things of that type
(the population as a whole).
 Inductive generalizations have the following
form:
• Z percent of observed F's are G.It is probable, therefore, that Z
percent of all F's are G.
• For example, we want to know what percentage of students at a
particular college are in favor of abolishing the death penalty.
• Clearly, it would be extremely difficult to ask every student at the
college whether they favor abolishing the death penalty. What we can
practically do is to select a sample of students and determine their
position on the issue, and then to generalize the results to the whole
student body.
 An inductive generalization could be written
out as follows:
Sixty-five percent of students at X College
who were questioned are in favor of abolishing
the death penalty.
It is probable, therefore, that sixty-five
percent of all students at X College are in favor of
abolishing the death penalty.
 Another example
• Observation: Suppose you observe several students from a
particular high school, and you find that all of them excel in
mathematics.
• Inductive Generalization: From this specific observation, you
might make a generalization that "students from that high
school are good at mathematics.“
• Conclusion: Therefore, you might conclude that the high
school has an effective math program or teaching method.
• Observation: You observe that over the past month, every
time your neighbor plays loud music late at night, the
following day there is an increase in the number of birds in
your backyard.
• Inductive Generalization: From this specific observation, you
might make a generalization that "loud music attracts more
birds to the area.“
• Conclusion: Therefore, you might conclude that there is a
correlation between loud music and the presence of birds in
your backyard.
 Evaluating Inductive Generalization
• There are two important questions we must ask when it comes to
determining whether inductive generalizations are strong or weak:
Is the sample large enough?
• The size of the sample population is an essential factor in
determining whether the conclusion about the population as a
whole is justified or not.
• A sample is "large enough" when it is clear that we have not rushed
to judgment, that we have not formed a hasty generalization.
Admittedly, this business of specifying what we mean by "enough" is
not easy, but oftentimes our common sense can help us decide
when the sample is large enough.
• As a rule of thumb, the more examples you find, the stronger your
argument becomes.
• One thing that we need to consider in determining the
sufficiency of the quantity of the sample is the quantity of
the whole population. In our example regarding the
students' position on death penalty, we must know around
how many students are there in the college. Suppose there
are around three thousand students, a sample of twenty
students is clearly insufficient. A hundred students, however,
may already be enough. Adding fifty more will increase the
strength of our argument.
•Suppose we have three hundred students
for our sample, will that make the
conclusion of our generalization acceptable?
•Not necessarily.
•Although the sample is definitely large enough,
there is another factor we need in evaluating the
strength of inductive generalization. Thus, we
need to ask another question.
 Is the Sample Representative?
• Although there were three hundred students who were interviewed
for the survey, the generalization may be weak if the three hundred
students only represent a particular portion of the whole student
population. Suppose these three hundred students are all members
of Christ Youth in Action (CYA), a Catholic organization of young
people, they may not actually represent the whole student
population if a significant number of students in that college is not a
member of that organization. This will make our conclusion
questionable since such membership to that organization greatly
influences one's view on death penalty for the Catholic Church
strongly opposes such kind of punishment. We call that kind of
sample a biased sample.
• One way to ensure sufficient relevant diversity is by
making the sample random. A random sample is "one
in which all members of the target have an equal
opportunity to be in the sample. "For instance, you
could interview members randomly by choosing every
fifth or tenth name on the membership list. Another
possibility would be to randomly interview people in a
common meeting place. The aim of creating a random
sample is to ensure that the diversity of the target is
reflected by the sample. It will not be a random
sample if it excludes part of the target.
• Samples may also be biased when surveys require
participants to initiate contact rather than using a
survey taker to actively solicit responses. For example,
surveys requiring that participants respond by sending
a text message, going online, phoning in their
response are likely to get unrepresentative results
since the respondents are self-selected. Only people
who are particularly interested in the issue are likely
to respond to the survey. To make matters worse,
unless surveys prevent respondents from contributing
their answers more than once, the data is likely to be
skewed by unscrupulous repeat respondents who are
trying to influence the outcome.
•We can observe that when established survey
companies in the Philippines (like SWS, Pulse
Asia) conduct opinion polls, they usually have
only around 1,000 to 1,200 respondents to
represent the whole Filipino population (which is
more than ninety million at present). But these
surveys are usually reliable since the sample
taken is random representing different sectors or
groups of the whole population, that means, the
respondents come from the different regions of
the country, socio-economic classes, age groups,
and so on.
• When we cannot do much about our sample (such as increasing it),
we can make our generalization acceptable by formulating an
appropriate conclusion. A good inductive argument should make a
conclusion that is appropriate to the evidence offered by its premises.
The conclusion should not claim more than its premises can support.
For example:

All ten of the Malaysians I met are good in business.

So, most Malaysians are good in business.

• Here the conclusion claims that most Malaysians are good in

business. But its premise only cited ten Malaysians who are good in
business. We could make the argument strong by making our
conclusion less sweeping, that is, the conclusion could cover less
ground.
• For example, if we instead say:
All ten of the Malaysians I met are good in business.
So, many Malaysians are good in business.
• the argument would be strong. Given our premise, the
conclusion is more likely to be true if its claim is more
limited, restricting itself to many rather than most
Malaysians. Other phrases that could soften the conclusion
are possible, probably, and likely. Remember that inductive
generalizations should not overstate their conclusions.
Let us take another example:
None of the ten teachers I met in this school knows how to speak
Spanish.
So, no teacher in this school knows how to speak Spanish.

We can see that the conclusion is so sweeping that the argument

is not strong. After all, if there is just one teacher in that school who
knows how to speak Spanish, the conclusion will easily be falsified. To
play it safe, then, we might conclude instead that "Very few, if any,
teachers in this school know how to speak Spanish." This is still a
sweeping conclusion, but it allows for the possibility of a few
exceptions. This makes the conclusion likely to be true, and thus the
argument is strong.
 Analogical Arguments
• Another type of inductive argument most commonly used in law is
analogical argument. Analogy is "a comparison of things based on
similarities those things share. We find analogies everywhere.“
• In college entrance examination, analogies are often given. Brother is
to sister as uncle is to? If your answer is "auntie," you are correct
because the relationship is one of opposites. We also encounter
analogies in poems and songs.
Perhaps love is like a resting place,
A shelter from the storm,
It exists to give you comfort,
It is there to keep you warm...
• Indeed, most of our everyday reasoning is based on analogy.
Joan reasons that her new pair of shoes will be durable on
the grounds that her other shoes with the same brand and
make have been durable. In the same way, Victor infers that
he will enjoy the action movie he is going to watch tonight
because it has the same director and leading actors as the
past action movies he enjoyed. Analogy is at the basis of
these simple, ordinary arguments we make.
• What makes an argument by analogy? Analogical arguments
depend upon an analogy or a similarity between two or
more things. Analogies compare two or more things;
arguments by analogy go one step further. They claim that
another similarity exists, given the similarities already
recognized. Whereas analogies simply point out a similarity,
arguments by analogy claim that certain similarities are
evidence that there is another similarity (or other
similarities). This type of reasoning has a simple structure: A
and B have characteristic X. A has characteristic Y. Therefore,
B has characteristic Y.
• The expert draws an analogy; an argument based upon that analogy
leads to the conclusion about causal connection that aims to resolve
the dispute at hand. Analogical reasoning is also the basis of what we
know as "circumstantial evidence".
• In prosecuting a defendant for a crime of theft, for instance, the legal
counsel may not be able to provide direct evidence proving that the
accused personally stole the item but he may present evidences that
are entirely circumstantial such as the defendant fingerprints at the
scene of the crime and the fact that the defendant was found with a
large amount of money without being able to give any acceptable
reason. The judge will draw conclusion from such evidences about
who has stolen the item based upon his/her knowledge of the history
of people's actions in other cases.
• Circumstantial evidence is sufficient for conviction if:
1. a) There is more than one circumstance;
2. b) The facts from which the inferences are
derived are proven; and
3. c) The combination of all the circumstances is
such as to produce a conviction beyond
reasonable doubt.
 Evaluating Analogical Argument
• Just as there are good or bad analogies, there are also good
and bad analogical arguments. One of the fallacies of
reasoning is called fallacy of false analogy (which will be
discussed later in the chapter on fallacies). It results from
comparing two (or more) things that are not really
comparable. It is a matter of claiming that two things share a
certain similarity on the basis of other similarities, while
overlooking important dissimilarities.
• The first criterion to be considered in the evaluation of an analogical
argument is the relevance of similarities. Consider, for example, the
following analogical argument:

Arizona signed into law the toughest bill on illegal immigration in

generations, making the failure to carry immigration documents a
crime. We can expect that New Mexico will soon pass a similar law.
After all, New Mexico is a lot like Arizona, given that both have a large
population of immigrants, and both are bordered by Mexico.
• In this example, New Mexico is compared to Arizona. The arguer has
identified two ways in which they are similar: they both have a large
population of immigrants and are bordered by Mexico. In evaluating
this analogical argument, we must consider whether the similarities
cited between the two states are relevant or irrelevant to the
conclusion of the argument. The issue is whether New Mexico will
likely pass a similar immigration law. With respect to this issue, the
similarities identified by the arguer are relevant to the conclusion of
the argument.
• However, suppose one will make the same conclusion based on the
premise that Arizona and New Mexico are both under a Republican
governor and both have several national forests and parks. This will
not be a good argument because, though there are cited similarities
between the two states, these similarities have little bearing on the
issue of passing a strict immigration law.
• Another important criterion by which analogical argument
may be judged has to do with the relevant dissimilarities
between the entities being compared. Consider this
example:

President Clinton's actions are "not just about sex," but

constitute "obstructions of justice," just as former President
Nixon's actions were. Both Nixon and Clinton lied about their
conduct in trying to cover up an improper conduct, and Clinton
even did it under oath. If Nixon's actions were impeachable,
Clinton's should also be.
• This seems to be a good analogical argument as the similarity cited
has a bearing on the issue of whether or not Clinton's actions are
impeachable. However, one can refute an analogical argument by
citing a relevant difference that exists between the entities compared
which can weaken the argument's conclusion. In the case above,
Clinton's side can argue that Nixon's lies were made in an attempt to
cover up a criminal action, break-ins, destruction of property, and
other acts related to abuses of presidential power or abuses of the
presidential office, whereas Clinton's actions are related to
consensual sex, were not an abuse of presidential power, and
everyone lies about sex, so that his acts did not rise to an
impeachable offense.
• It is important in analogical reasoning to check how similar and how
different the facts are in various cases. If the facts are substantially
similar the outcome of the cases will not be different. But if the facts
have relevant differences, the outcome in one case will not be the
same in another case.