# Quality Progress - May 2017 - 27

```of Linda being a bank teller, however, are higher
than the odds of Linda being both a bank teller
and a feminist.
Think of it this way: If the odds of Linda being
a bank teller are 40%, and the odds of Linda
being a feminist are 70%, the odds of Linda being
both a bank teller and a feminist are only 28%,
while the odds of her being a bank teller are still
40%. The more complex hypothesis ("Linda is a
bank teller and feminist") requires more assumptions that must be true for the hypothesis to be
correct.
There is a simple solution to the problem of
overly complex hypotheses: Occam's razor, which
states that when faced with two hypotheses that
explain the data equally well, you should choose
the simpler.7 The hypothesis that makes fewer
assumptions is more likely to be correct than one
that makes many assumptions, as illustrated in
the Linda problem.
This does not mean additional assumptions
can't be evaluated as different hypotheses. One
of many hypotheses could and should be correct.
Combining the assumptions into one convoluted
hypothesis, however, risks the hypothesis being
wrong.

Keep it simple

Modesty in a hypothesis transitions into simplicity.8 A hypothesis should be simple in that it does
not go far beyond the data at hand.
Suppose parts are being detected with a deviation in length. The hypothesis that the operator
needs retraining would not be a simple hypothesis without any prior evidence of operator training
being related to the problem.
It would make more sense to use the simpler
hypothesis that a setting on the cutting machine
is wrong. If the setting on the machine is wrong, a
new hypothesis could be that the operator made
a mistake. Only if the second hypothesis is correct
would the hypothesis that the operator needs
retraining be appropriate.
Even then, the second hypothesis should
be evaluated because there could be other
reasons-such as an incorrect work order-to
explain why a well-trained operator might
make a mistake. Simplicity in a hypothesis can
be achieved by avoiding extrapolating too far
beyond the data at hand.

TA B L E   1

5 virtues of a hypothesis as applied
to root cause analysis
Virtue

Description

Conservatism

Should conflict with few pre-existing beliefs.

Modesty

Should make few assumptions.

Simplicity

Does not go far beyond the data at hand.

Generality

Applies to a wider range of situations.

Refutability

Must be testable.

Source: William Van Orman and J.S. Ullian, The Web of Belief, 10th edition,
McGraw-Hill Education, 1978.

Generality is preferable

A general hypothesis is one that applies to a wide
range of situations.9 The virtue of generality is
more applicable to hypothesis-forming in science,
but it does have some relevance to RCA.
Although a hypothesis should be simple and
modest, it also must be general enough to be
applied. A hypothesis that is valid only under controlled test conditions, for example, would not be
useful for an RCA in which the failure is happening under real-world conditions.
A sufficiently general hypothesis can be used
for making predictions. A hypothesis that units
are failing due to variation, for example, leads to
the prediction that units without variation will not
fail. A hypothesis that does not make a prediction
can't be tested, and an untestable hypothesis
should not be trusted.

Refutability required

For a hypothesis to be valuable, it must be possible to refute it. An irrefutable hypothesis serves
little purpose because there is no way to tell
whether it is correct. This is comparable to Karl
Popper's falsification, which requires a hypothesis
to be falsifiable,10 meaning it must be possible to
prove a hypothesis wrong.
Popper believed a hypothesis also can be disproved at a later date if more evidence becomes
available and a new hypothesis is supported by
much corroborating evidence.
A hypothesis can be refuted by testing it.
John R. Platt recommended, "Devise a crucial

qualityprogress.com ❘ May 2017

QP 27

```
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Seen and Heard
Progress Report
Field Notes
Mind the Gaps
Solid Footing
Are These the Same?
New Tricks for an Old Tool
Innovation Imperative
Experience More
Career Coach
Statistics Spotlight
Standard Issues
Marketplace
Footnotes
Back to Basics
Quality Progress - May 2017 - intro
Quality Progress - May 2017 - cover1
Quality Progress - May 2017 - cover2
Quality Progress - May 2017 - 1
Quality Progress - May 2017 - 2
Quality Progress - May 2017 - 3
Quality Progress - May 2017 - 4
Quality Progress - May 2017 - 5
Quality Progress - May 2017 - Seen and Heard
Quality Progress - May 2017 - Expert Answers
Quality Progress - May 2017 - Progress Report
Quality Progress - May 2017 - Mr. Pareto Head
Quality Progress - May 2017 - 10
Quality Progress - May 2017 - 11
Quality Progress - May 2017 - Field Notes
Quality Progress - May 2017 - 13
Quality Progress - May 2017 - 14
Quality Progress - May 2017 - 15
Quality Progress - May 2017 - Mind the Gaps
Quality Progress - May 2017 - 17
Quality Progress - May 2017 - 18
Quality Progress - May 2017 - 19
Quality Progress - May 2017 - 20
Quality Progress - May 2017 - 21
Quality Progress - May 2017 - 22
Quality Progress - May 2017 - 23
Quality Progress - May 2017 - Solid Footing
Quality Progress - May 2017 - 25
Quality Progress - May 2017 - 26
Quality Progress - May 2017 - 27
Quality Progress - May 2017 - 28
Quality Progress - May 2017 - 29
Quality Progress - May 2017 - Are These the Same?
Quality Progress - May 2017 - 31
Quality Progress - May 2017 - 32
Quality Progress - May 2017 - 33
Quality Progress - May 2017 - 34
Quality Progress - May 2017 - 35
Quality Progress - May 2017 - New Tricks for an Old Tool
Quality Progress - May 2017 - 37
Quality Progress - May 2017 - 38
Quality Progress - May 2017 - 39
Quality Progress - May 2017 - 40
Quality Progress - May 2017 - 41
Quality Progress - May 2017 - 42
Quality Progress - May 2017 - 43
Quality Progress - May 2017 - Innovation Imperative
Quality Progress - May 2017 - 45
Quality Progress - May 2017 - 46
Quality Progress - May 2017 - Career Coach
Quality Progress - May 2017 - 48
Quality Progress - May 2017 - 49
Quality Progress - May 2017 - Statistics Spotlight
Quality Progress - May 2017 - 51
Quality Progress - May 2017 - 52
Quality Progress - May 2017 - 53
Quality Progress - May 2017 - Standard Issues
Quality Progress - May 2017 - 55
Quality Progress - May 2017 - 56
Quality Progress - May 2017 - 57
Quality Progress - May 2017 - Marketplace
Quality Progress - May 2017 - 59
Quality Progress - May 2017 - Footnotes
Quality Progress - May 2017 - 61
Quality Progress - May 2017 - 62
Quality Progress - May 2017 - 63
Quality Progress - May 2017 - Back to Basics
Quality Progress - May 2017 - cover3
Quality Progress - May 2017 - cover4
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