A review of the importance of risk in our decision-making and how statistics can be used to measure that risk.

1. Section & Lesson #:
Pre-Requisite Lessons:
Complex Tools + Clear Teaching = Powerful Results
Risk Analysis: The Reason We Use Statistics
Six Sigma-Overview – Lesson 2
A review of the importance of risk in our decision-making and how
statistics can be used to measure that risk.
Six Sigma-Overview #01 – Problem Resolution using DMAIC
Copyright © 2011-2019 by Matthew J. Hansen. All Rights Reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted by any means
(electronic, mechanical, photographic, photocopying, recording or otherwise) without prior permission in writing by the author and/or publisher.

2. The Effects of Risk
o Risk affects every decision we make!
• Would you drive your car if you knew there was a high risk it would break down?
• Would you eat at a restaurant that you knew had a high risk of food poisoning?
• Remember Hannah’s strep throat? There could have been risks in finding & fixing the root cause.
o Prudent business decisions should assess and measure risk.
• Let’s use an example of a glass of water…
o Risk has an inverse relationship with confidence.
• Data (i.e., proof, evidence, etc.) builds confidence; the lack of data (i.e., assumptions) creates risk.
o To reduce risk and build confidence, get data!
• Nearly all statistical tests measure risk (typically reflected as the P value).
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The empty portion
(air) represents
assumptions (risk)
The filled portion
(water) represents
data (confidence)
The entire glass
represents our
available understanding
for making decisions
Just as adding
water displaces air,
adding data displaces
our assumptions.
Copyright © 2011-2019 by Matthew J. Hansen. All Rights Reserved. No part of this publication may be
reproduced, stored in a retrieval system, or transmitted by any means (electronic, mechanical, photographic,
photocopying, recording or otherwise) without prior permission in writing by the author and/or publisher.

3. Method 2:Method 1:
Methods for Gathering Data
The overall goal of statistics is to use a sample to make inferences about a population.
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For example, what if I want
to know how many red jelly
beans there are in a jar?
There are two general
methods to answer this….
Empty out all the jelly beans and
manually count all the red ones
Pro: More Accurate
Con: More Time
Method 2 uses the concept of statistics where a small portion (sample) is used to
make an estimate (inference) across an entire group (population). But is Method 2 better?
Take out a small portion of jelly beans,
count the red ones and multiply by the
proportional volume in the jar
Pro: Less Time
Con: Less Accurate
Copyright © 2011-2019 by Matthew J. Hansen. All Rights Reserved. No part of this publication may be
reproduced, stored in a retrieval system, or transmitted by any means (electronic, mechanical, photographic,
photocopying, recording or otherwise) without prior permission in writing by the author and/or publisher.

4. Risks, Rewards and Constraints
o Is Method 2 better? Maybe – it depends on RISK.
o Evaluate the CONS for each method; these represent their risk:
• Method 1 CON: How much more time will it take to manually count the red jelly beans?
• Method 2 CON: How accurate do I have to be in my estimate of the red jelly beans?
o Compare the risks and rewards.
• RISK: Time vs. Accuracy – which is more important?
• REWARD: Which method would you choose if the prize was…
 A T-Shirt
 $100
 $1,000,000
o Consider the constraints.
• What if you were only given 1 minute to answer?
• What if the jar was 10 ft tall?
• What if you weren’t given access to the entire jar?
o How does Six Sigma deal with risk, rewards and constraints?
• Understanding the VOC helps us understand any constraints and balance risks with rewards.
• Statistical tools are designed to analyze data and evaluate the levels of risk.
• The goal is not to eliminate risk; the goal is to answer…
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Risk
Reward
How can I minimize risk and maximize rewards within the constraints?
Copyright © 2011-2019 by Matthew J. Hansen. All Rights Reserved. No part of this publication may be
reproduced, stored in a retrieval system, or transmitted by any means (electronic, mechanical, photographic,
photocopying, recording or otherwise) without prior permission in writing by the author and/or publisher.

5. Understanding Our Assumptions
o It’s very rare in life to have an opportunity to analyze a population.
• Measuring data across a population is always ideal, but not always practical.
• We rely on statistics for elections, market analysis, employee performance, etc.
o Prudent data analysis requires understanding our assumptions.
• We assume our sample of jelly beans represents the population.
 What if our sample only includes 10 jelly beans?
 How big should our sample be to ensure it represents the population?
 How do we know our method for collecting the sample is correct and random?
• We assume we know the volume of the jar.
• We assume the jelly beans in the jar are evenly mixed by color (random).
• What’s the risk if we don’t consider these assumptions before
we collect our sample?
 We may be making very wrong conclusions leading to very wrong actions.
o Assumptions are a primary form of risk.
• When we validate our assumptions, we’re reducing risk and increasing our confidence.
Copyright © 2011-2019 by Matthew J. Hansen. All Rights Reserved. No part of this publication may be
reproduced, stored in a retrieval system, or transmitted by any means (electronic, mechanical, photographic,
photocopying, recording or otherwise) without prior permission in writing by the author and/or publisher.
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6. Practical Application
o Have you ever said to yourself “If I only knew then what I know now”?
• This phrase generally infers a previous situation when we made a wrong or uninformed decision.
• Think of at least 2 situations like that in your personal or work life.
 For each of those situations, try to answer the following:
– What critical information would’ve helped me make a different (or better) decision in that situation?
– How could I have acquired that critical information in that situation, if at all?
– If I were in that similar situation today (with the same limited knowledge), would I do anything differently in my decision
process?
» If not, then why not?
» Or if so, then what is different in my decision process?
 Based on your answers above, what does this reveal about how you balance risk vs. reward in your
decision process? (E.g., are you more prone to make quick, “risky” decisions?)
Copyright © 2011-2019 by Matthew J. Hansen. All Rights Reserved. No part of this publication may be
reproduced, stored in a retrieval system, or transmitted by any means (electronic, mechanical, photographic,
photocopying, recording or otherwise) without prior permission in writing by the author and/or publisher.
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