Inductive Reasoning

Inductive reasoning involves making useful
generalizations about the environment as a whole, based on a
necessarily limited number of observations.
As such, it is an
important tool that people use to build the models of reality they
need to function effectively.

While conclusions can be wrong if observations are faulty or are
drawn from an unrepresentative sample, if properly used, inductive
reasoning can be incredibly powerful. Indeed, it lies at the root of
the scientific method that has done so much to advance humanity in
the last 500 years. Properly-applied scientific method is inductive
reasoning in its purest form.

At the core of inductive reasoning is the ability to look at
outcomes, events, ideas and observations, and draw these together to
reach a unified conclusion. Considering this, an experienced
business person can use his or her own experiences to draw
conclusions about current situations and solve problems based on
what he or she has known to work in the past in similar situations.

By accepting conclusions derived from inductive reasoning as "true"
(in a practical sense), good managers can build on these conclusions
and move forward effectively and successfully.
How to Use the Tool
Much inductive reasoning happens intuitively and
automatically. Without it we just couldn't function: Everything we
did would have to be subject to so much analysis and consideration
that we'd "grind to a halt".

At the other extreme from this intuitive inductive reasoning, we
have the formal scientific method we're taught at school:

Formal statement of a hypothesis, a suggested generalized "truth.
Design of an experiment – a series of tests or observations designed
to test the hypothesis in a fair and reasonable range of different
circumstances, and with complicating factors "controlled" as far as
possible.
Proper documentation of the tests carried out (the "Method") and the
"Results" of the experiment, so that these can be subjected to later
scrutiny.
Drawing of "Conclusions", technically establishing whether the
hypothesis has been shown to be false or not, but in practice
establishing whether the hypothesis is likely to be true.

(This is also where ideas of statistical significance become
important, in mathematically evaluating how good conclusions are
likely to be.)

And in between these two extremes, we have different levels of
formality of inductive reasoning that circumstances allow and
demand.

Tip 1:
The formality of the process used to reach conclusions largely
depends on the scale of the problem being solved.

Where an issue affects very many people significantly and
reasonable time is available, then reasoning had better be
thoroughly researched and checked – otherwise, huge errors can
be made.

However, this research and checking can be slow and costly, and
can cause "paralysis by analysis". Small decisions, or those
needing quick resolution, often will not allow detailed
research. This is where a less formal (and necessarily more
error-prone) approach is necessary, and is why experience is
important in good business decision making.

This is not, however, an easy get-out for lazy decision making:
You need to do as much research as you can with the resources
and time available.
Tip 2:
Experience speeds inductive decision making, and makes it more
robust and more likely to be correct. On the other hand, people
relying on experience too much can miss important new factors in
a changing environment. This is where businesspeople must keep
at least a certain amount of open-mindedness, and need to keep
scanning the environment for change.
Tip 3:
There are many different types of inductive reasoning, including
simple induction, casual inference, prediction, argument from
analogy, generalization and statistical syllogism. These are
beyond the scope of this article.

Example
In the past, when we offer customers the
chance of winning a prize if they fill in a questionnaire, we get a
9% response rate. If we don't offer a prize, we get a 3% response
rate. We're going to tell customers who respond to our latest survey
that respondents will go into a free prize draw, so we'll get a
response of around 9%.

Using inductive reasoning, past experience is being applied to a
future situation to predict an outcome. The past experience that is
being applied seems to be appropriate, but there is no guarantee
that it will definitely apply in the future. It may be that
customers have got bored of filling in questionnaires and won't
reply at the same rate this time.


Deductive reasoning
An alternative to inductive reasoning is deductive reasoning .
While inductive reasoning starts with facts and induces a rule
from them, which is then used to make an estimate of what will
happen in the future, deductive reasoning works the other way.
It starts with a rule, that has been proven beyond all
reasonable doubt, and calculates an outcome from that. For
example, if a bank offers a 4% interest rate, and you deposit
$100 for a year, you will get $104 at the end of the year. Using
inductive reasoning to calculate what you investment would be
worth after a year, you would ignore the currently advertised
interest rate and say, "Last year, I got $6 interest on my $100
investment, so I expect to get the same this year."


Key Points
Inductive reasoning is the process by which we
make a necessarily limited number of observations and seek to draw
generalized conclusions from them.

The most rigorous inductive reasoning is conducted through formal
experiments, much as we're taught at school.

However, decisions which are small in scope or need to be made
quickly often do not allow the overheads of extensive method and
experimentation: Here we have to accept that the "truths" we seek to
establish may be faulty, and generalizations may be misleading. This
is where management of uncertainty becomes important.