Expected Value Makes Uncertainty Manageable

Ever face a late-stage design decision where your gut says “maybe,” finance says “no,” and the schedule says “hurry”? 

We unpack a simple way to make those calls with more clarity: using expected value to connect confidence, upside, and downside into one sober view of net benefit. No jargon, no spreadsheets required—just a clear framework that helps you see when a $50,000 test buys real certainty, and when the right move is to ship.

Still, numbers don’t get the final say. The goal isn’t to pick the biggest EV; it’s to choose the most balanced, actionable, project-aligned option.

If this approach helps you navigate the gray areas between risk and reward, follow the show, share it with a teammate, and leave a quick review so others can find it. Got a decision you’re wrestling with? Send it our way—we’ll feature it in a future breakdown.

This blogpost: https://deeneyenterprises.com/qdd/podcast/expected-value-makes-uncertainty-manageable/

Facing a really complicated and nuanced decision? Try this Method to Help with Complex Decisions (DMRCS)

Ready to apply this to your project?
→ Schedule a free discovery call: Dianna's calendar

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Learn the full framework:
→ Get the Book: Pierce the Design Fog

ABOUT DIANNA
Dianna Deeney is a quality advocate for product development with over 25 years of experience in manufacturing. She is president of Deeney Enterprises, LLC, which helps organizations and people improve engineering design.

Episode Transcript

SPEAKER_00 (00:00):
You're trying to make a decision and you're 75%
confident, but then your managersays, Well, can we run another
test?
The test would be$50,000.
Do you do it?
Now we're talking aboutcombining uncertainty and money
and costs of failures.
I'm going to share with you alightweight decision tool that

(00:21):
you can use to help, calledExpected Value.
More after this briefintroduction.
Welcome to Quality DuringDesign, the place to use quality
thinking to create productsothers love, for less time, less
money, and a lot less headache.
I'm your host, Diana Deaney.
I'm a senior quality engineerwith over 20 years in
manufacturing and productdevelopment and author of Pierce

(00:44):
the Design Fog.
I help design engineers applyquality and reliability thinking
throughout product development,from early concepts through
technical execution.
Each episode gives youframeworks and tools you can
use.
Want a little more?
Join the Substack for monthlyguides, templates, and QA where
I help you apply these to yourspecific projects.

(01:06):
Visit qualityderingdesign.com.
Let's dive in.
Welcome back.
We are in our third and finalphase of an arc that we've been
taking about decision making andengineering.
We're faced with a late stagedesign problem and we need to
improve our confidence in it andthen make a decision.

(01:28):
Our framework that we've beenusing is frame it, then
investigate it, and now finallychoose it.
I've been writing in-depth andproviding examples on Substack
with this system.
You can find the articles there.
You can also listen to theprevious five episodes of this
podcast, which cover similartopics.

(01:51):
And now we're wrapping it upwith a final podcast episode
related to this series, for now,anyway.
When you're working in aregulated industry, we're
usually focused on performanceand doing what it takes in order
to make sure that a productmeets the performance that it
needs to meet.
I'm thinking things likeautomotive and medical device

(02:11):
applications.
Even in those environments,we're still designing for
customers and there are costs toconsider, business decisions
that we may need to make.
If you have an MBA or work inaccounting, you're probably
familiar with expected value.
Expected value measures theaverage outcome you can expect
from a decision.

(02:32):
What I especially like about itis that it combines our
uncertainty about a decision orour certainty about a success,
and combines that with the valueof our success and the cost if
we should fail, which are twodifferent values.
Expected value combines thesethings into a net benefit or

(02:54):
loss of a choice by combiningthe probability of success with
its upside value and subtractingthe probability of failure
multiplied by its downside cost.
The probabilities that we'retalking about here we've been
developing in the last twophases and frame it and
investigate it.
That's our confidence level inour decision that we need to

(03:15):
make.
If we are 75% confident thatit's going to work, then that's
our probability of success andour expected value calculation.
And that value of success mightbe related to things working as
intended.
Maybe reduce costs, faster timeto market, or increased revenue.
So then what do we do about ifit doesn't work?

(03:37):
If we're assigning a probabilityof success of 75%, that's our
confidence in our success, wellthen the probability of failure
is one minus that, which is 25%.
So we've already got thatfigured out.
So now what would be the cost ofour failure?
That would be not meeting ourrequirements.
That could be rework, delays,lost opportunities, those sort

(04:00):
of things.
Expected value is a lightweightdecision tool that helps us
combine these things together.
All right, well, we think oursuccess is going to be this and
we're this confident in it.
But then again, if we fail, thisis what we could lose.
Now, how do we know what kind ofcosting to put in this equation?
Well, that's again anotherbeautiful part about our

(04:22):
framework, our frame it andinvestigate it, choose it.
Through this cycle, we'relearning more about our problem
and what effect it has on theproject.
And we're quantifying the impactthat this decision has on the
project at the beginning.
And we're learning more aboutwhat the solution should be.
So heading into this last phaseof choose it, we have a lot of

(04:45):
information and data to help usassign costs for expected value.
Let me run through an example,the example that I use on
Substack also.
We had a problem with a part.
We weren't sure if we shouldinjection mold it or not.
We weren't feeling confidentabout our decisions about how
the design was going to look orwhat material it should be made

(05:06):
of.
But then coming out of ourinvestigative work, we decided
that we wanted to take a closerlook at nylon, for example.
That was one of the choices thatwe picked.
Nylon had a lot of otherbenefits to the other materials
that we were looking at.
We used paired comparison tokind of flush that out.
But now we say, all right, well,nylon, we were the least

(05:26):
confident in his performance.
We had a 60% confidence in nylonperforming like we wanted it to.
Should we run an additional$50,000 test to increase our
confidence to 75%?
Our success is defined as themold or the product working as
intended.
And that's going to add, weestimated,$500,000 in value

(05:51):
through those things likereduced cost, faster time to
market, increased revenue.
Failure we define as the molderproduct not meeting
requirements, and that wouldincur$165,000 cost through
rework of the mold, delays tomarket, and lost opportunities.
Success is$500,000 and ourfailure is$165,000.

(06:17):
Our probability of success isbased on our updated confidence
levels from our investigativework.
Our confidence in nylon is 60%.
So then the probability offailure is simply 1 minus 60%,
which is 40%.
Now let's plot that into ourexpected value equation.

(06:37):
So the expected value is theprobability of success times the
upside minus the probability offailure times the downside.
So we have probability ofsuccess times the upside is uh
60% times$500,000, which is$300,000.
And the probability of failuretimes the downside is 40% times

(07:01):
165K, which is 66K.
So our expected value, if weproceed with nylon, is$234,000.
So it's interesting to see it'sa positive number.
But our real question is shouldwe invest in the$50,000 testing
to raise our confidence in thismaterial selection to 75%?

(07:25):
Only looking at expected valuefor now, we run the numbers
again.
And we use the same monetaryvalues, except now our
probability of success, we'rethinking, will be 75%, and
probability of failure is, ofcourse, 25%.
We're also going to subtract thecost of test, which is the 50K.

(07:47):
So our expected value rounded upcomes out to$284,000.
If we just proceed with what wegot,$234,000, and if we invest
$50K to raise our confidence to$75%, even subtracting the cost
of the test, our expected valueis higher,$284.

(08:08):
So yes, it's worth testing if weonly care about expected value.
In our ultimate decision makingportfolio, we're not only
considering expected value, butit is an aspect of it.
And doesn't that provide a lotmore clarity?
I mean, you you barely know whatthe problem was in the first
place.
We had a molding problem, we'retrying to make a material

(08:31):
decision.
What's it for?
Where's our project at?
Well, just having this net valueand considering our confidence
in our decision gives us betterinformation to be able to make a
better informed decision, don'tyou think?
I think so, which is why I likeit, which is why I'm telling you

(08:52):
about it.
But it is really only one pointof data.
Projects are more complicatedthan that.
Products are more complicated.
We know that if we pursueadditional testing, that's not
going to just cost us testing.
It's also additional time.
We were still considering twoother materials, is it still
worth it?
So there's more things that weneed to look into, more points

(09:16):
of interest that we need toconsider.
And depending on your project,maybe the impact to your
project, um, wouldn't beacceptable to take any risks or
to accept a confidence of 60%.
So whereas this is worth doing,especially as engineers, it
helps us to wrap in the businessside of our decision making.

(09:38):
And also helps involve us in theconversation with our project
managers and our projectmanagers.
We don't want to use expectedvalue when the downside risk is
existential, meaning it'scompany killing.
Some risks you just don't take,even if the expected value is
positive.
If you're in that regulatedindustry where the best effort

(10:00):
has legal meaning beyondexpected value.
And if the uncertainty is sohigh, like below 30% confidence,
that the numbers aremeaningless, then we need to do
some more investigation toreally figure out more options
about our decisions.
But here's the thing, this is amath equation, but it's not a

(10:20):
math problem, is it?
These are complicated decisionswith real impact, which is why
we get a stomachache about them.
This framework guides you fromknee-jerk reactions and gut
instinct to a systematicapproach for data and
information you need to make abetter informed decision.

(10:41):
So stop, evaluate, gatherinformation, focus on what
matters for your project.
That goes a long way towardmaking the right decision.
And document how you got there.
Your phase one framing, phasetwo evidence stack, phase three
rationale.
That document is your learningasset.

(11:01):
When a systematically madedecision doesn't work out, you
have something valuable, adocumented trail of what
evidence you had, what youexpected, and where reality
diverged.
And this turns not into a CYAdocument, but it turns failure
into organizational learning.
What did we do this time, orwhat did we assume this time

(11:22):
that we now know better not toassume the next time?
And when using expected value,don't choose the highest option.
Choose the most balanced,actionable, and project-aligned
option.
That's what makes it smart, notjust mathy.
If you want to go deeper intothese concepts that we've been
talking about the last fewmonths, visit quality during

(11:45):
design.substack.com.
There are six posts totalrelated to this three-phase
system.
The most instructional posts arethe Ask Me Anything, and they're
boosted by the StrategicInsights, looking at how people
actually apply these in the realworld.
For example, in this month, welooked at Tesla's battery design

(12:06):
decisions.
Not that we have the insidescoop or the inside financials
and decision making that Teslauses, but we did look at the
changes that they made.
Like why did they pivot frompursuing the most
technologically advanceddecision?
And we use these lightweightdecision frameworks to kind of

(12:26):
demonstrate how it all workstogether and how they could have
come to those decisions.
So, quality during design.com.
I'll be posting more articleslike that throughout the next
year.
And this is our final episode of2026.
So I wanted to thank you forbeing a listener for finding
quality during design andjoining me this last year.

(12:49):
I look forward to more of it in2026.
Happy New Year.