Technology Development Network

Coming May 2022 New Book on Technology Development

    Technology Development

    Lessons from Industrial Chemistry

    and Process Design

 

    by Ron Stites

 

 

 

Ron has recently completed the manuscript for a new book on Technology Development under contract with Walter de Gruyter GmbH. De Gruyter is a German scholarly publishing house specializing in academic literature. The new book should be available May of 2022. The Table of Contents below gives you an idea of the topics covered:

    Chapter 1: Introduction

    Chapter 2: What is Technology and Technology Development?

        Some Common Misconceptions

        Types/Classes of Technology

        B2B Markets

        Consumer Markets

        Government Markets

        Social/Common Good

        Impact on Existing Markets

        The Benefits of Technology Development

    Chapter 3: Some Basics Axioms of Technology Development

        Axiom 1 – Nothing is Completely Predictable

        Axiom 2 – Collaboration can be a Four-Lettered Word

        Axiom 3 – Money is the Final Unit of Measure

        Axiom 4 – Everything is Urgent

        Axiom 5 – Size Matters

        Axiom 6 – The Quest for Knowledge Requires Intuition and Deduction

    Chapter 4: The Science of Technology Development

        Markets First

        Investors/Stakeholders Second

        Go/No-Go Decision Process Third

        Finally, The Technology Development Process

        Planning to Learn

        Designing Your Experiments

            The Intuitive Phase

            The Deductive Phase

            Making Sense of Uncertainty

            Simple Statistics – Population, Sample, Average, and Standard Deviation

            Guarding Against Major Blunders

        Execute Your Plan

        Evaluation and Reporting

        Improve Your Experiments or Wrap Up Your Conclusions

        Valuation Methodology (Present Value/IRR)

        Evidence-Based Decision-Making and Data Science

    Chapter 5: The Art of Technology Development

        The Art of Human Relationships

            With Employees

            With Funders and Potential Funders

            With Customers and Potential Customers

            With Stakeholders

        The Art of Matching Technology with Markets

        The Art of Monetizing the Technology

    Chapter 6: Funding and The Discipline of Technology Development

        How Discipline Impacts Funding and the Growth of Wealth

        The Biz Plan

        Funding in Large Organizations

        Funding in Small Organizations

        Technology Development Startups – A Peculiar Subset of Small Business

        The Funding Process

            Dispelling Some Myths about Raising Capital

            Assessing Capital Needs

            Set Capital Goals

            Detail Capital Goals and Strategy

            Identify Potential Capital Sources

            Guiding Principles for Contacting Potential Investors

            Structure Capital Offering(s)

            Manage the Capital Campaign

    Chapter 7: The Future of Technology Development

        Critical Technology Development Needs

        Capital Sourcing Challenges

        Evaluation of Technology

        Use of Capital

        Scarce Supply of Capital for Small Business

        Accountability

        Patience

    Chapter 8:  Where Do We Go from Here?

        Reducing the influence of academics and Higher Education

        Strengthening review of government Technology Development programs

        Specifically requiring statistically sound review

        Encouraging small, primary investments directly in small businesses

        Graduated Capital Gains Tax

        Capital Gains Tax on Non-profits

        Phasing out Non-profit Status for all Non-profits

        Phasing in Strict Financial Reporting for all Non-profits

        Reforming all of Education to make it eventually Competitive, For-profit

        Requiring Grade-appropriate Probability and Statistics Curricula

        Requiring Appropriate Field Proficiency Probability and Statistics STEM and Professions

        Deregulating Crowdfunding for Small, Technology Development Companies

    Chapter 9: Some Final Words

If you would like information on how to get a copy, contact us at Ron Stites at [email protected]

What is Data Science? Part II

Thinking in Bets by Annie Duke

Before we can get anywhere with Data Science, we must embrace "Thinking in Bets." We must conclude that:

• Not everything is predictable – Everything is probabilistic,
• Not everything is entirely chaotic – It is possible to evaluate and manage uncertainty, and
• There are advantages to managing uncertainty.

Unless we believe that we have some limited control over uncertainty, we will fall victim to one of two traps. We will either think we can completely control events or conclude that we have no control over events. Both positions are demonstrably false.

I highly recommend readers get the book Thinking in Bets by Annie Duke. Duke was a professional gambler. She learned through that experience that there are "smart bets" and "dumb bets.” She now applies those lessons to business and general decision-making. She is a business consultant and co-founder of The Alliance for Decision Education (see:  About the Alliance for Decision Education | Alliance for Decision Education ).

To use statistical inferences effectively, we must make good decisions in the face of insufficient information. We must set our decision points based on analyzing each decision's costs, benefits, and probable outcomes. This analysis becomes especially tricky when we cannot reduce the costs, benefits, and results to a standard measure such as money. Nevertheless, we should perform this analysis whenever practicable. Although a statistical analysis may not settle an argument, it will focus our attention on the "subjective" issues without conflating the discussion with irrelevant "facts." For example, if we could reduce a debate about widening a "dangerous" highway to something like, "Are you willing to spend $200 million for a 90% chance of reducing highway deaths by 0.5%," we can draw attention to affordability and potential compromise. We also plainly state the consequences of doing nothing and the probability that the proposed decision will not have the desired effect.

If we become committed to “thinking in bets,” we will naturally desire better objective data and employ more powerful decision-making tools. In addition, we will require uncertainty measures to accompany all data to “propagate” that error into predictions we make using those data. Thus, we will naturally ask relevant questions of Data Scientists.

Stites & Associates, LLC (SALLC) is a technology development group founded by Ron Stites in 1996. The scientists and engineers at SALLC strive to help clients succeed at technology development and deployment using Evidence-Based Decision-Making. Feel free to contact Ron Stites via email at [email protected].

New Direction for SALLC – Small Business Capitalization

Stites & Associates (SALLC) has long been a supporter, evaluator and developer of new technologies. These have ranged from new marketing techniques to new chemical processes. This has resulted in multiple start-up operations and five awarded patents.

One of the persistent problems with technology development has been funding that development. Frequently the initial ideas can be generated and even tested at a bench scale using personal funds, discretionary funds within organizations, small grants and loans. The problem becomes critical when the idea looks to have merit and needs real scale up testing and design.

The “valley of death” in raising capital is in the $500,000 to $5,000,000 range. This is too much money for those “informal” sources and too little for institutional investors. This might be a range for some government programs like NSF, DOE, DOD, USDA or even SBA but the risks are high, those programs are often too narrow, or they are more focused on political issues than on good science and economic success.

Many good ideas just die. That is a shame, but not the worst that can happen. Occasionally, an idea that “sounds good” but is flawed, grabs the attention of an overzealous investor and becomes a newsworthy, cringeworthy disaster – often with the help of naïve government support.

Ron Stites, the founder of SALLC, has founded a new entity, the Colorado Center for Innovation in Community Capital (the “Center”). This business is piloting a program in Colorado to supply capitalization support for small businesses. The idea is to advance the costs and provide the talent needed by small, innovative businesses to help them raise capital. This capital will be used to do the kind of testing and evaluating needed to determine if the “great” idea can be made into a sustainable business.

We are using the recent Opportunity Zone legislation to help kickstart this program. That legislation allows tax breaks to investors if they will rollover capital gains from past investments into businesses located in designated, economically depressed areas. This must be “patient money” in that the investors need to wait at least 10 years before taking out their money. This give the innovative company ample time to do the thorough evaluation, development and planning that is needed to turn the “good idea” into a great business.

We chose Colorado for several reasons, but one of these is that the selection of Opportunity Zones in Colorado was done quite well. There is a good blend of rural and urban areas, with many of the Zones having enough infrastructure to support small, innovative businesses. Hence, there are a wide variety of possibilities ranging from agricultural to housing to technological and scientific innovations.

We plan to soon expand beyond Colorado and Opportunity Zones. For now, our main focus is finding innovative companies in Colorado Opportunity Zones that are ready to take the next steps toward success.  These businesses may apply for support by completing a signup at:

Request Information

Each of these small businesses needs investors with capital gains that they might want to put to work supporting innovation and local economic development. If you are interested or just want more information, contact Ron Stites at [email protected] or visit:

Colorado Center for Innovation in Community Capital

 

Ron Stites is the Founder and Principal at Stites & Associates, LLC, and the Technology Development Network, a group of technical professionals who work with clients to improve decision making in technology driven companies by applying good management judgment based on objective evidence and sound scientific thinking. For more information see: www.tek-dev.net or email us at [email protected] .

What is Data Science? Part I

Among the hottest buzzwords in industry today are:

• Data Science
• Big Data
• Data Mining
• Statistics/Data Analytics
• Modeling
• Artificial Intelligence (AI)/Machine Learning

But what do they mean? Are they new, space-age discoveries that only a PhD in Statistics or Computer Science can understand?

Not really. Like so many things, the truth is hidden in a maze of jargon. The concepts are relatively simple and with the help of the personal computer, the calculations are much more manageable than in the past.

This little paper is designed to strip away much of the mystic and get down to the business utility of Data Science.

First – Some Background:

There are some things that have changed. The personal computer, the Internet, the rise of many, cheap methods for capturing data and using large databases have changed the landscape. It is now possible to capture incredibly detailed measurements about most processes, store them retrievably and analyze those data quickly and inexpensively. We have become obsessed with collecting data – mostly because we can. Data Science has recently come on the scene as an answer to the question, “Can we do anything useful with all these data?”

Data Science has been around since the Egyptians first started tracking the times and duration of the Nile flood and a bunch of other miscellaneous data. They first gathered information about the flood, crop practices, crop yields and the movements of the stars and planets. One of the first Data Scientists noticed that when the Star of Isis (Sirius) appeared to rise just before the sun, the Nile floods would start soon. Farmers planned their activities accordingly and the Egyptians flourished even though they lived in the center of a vast, dry wasteland. History indicates that Data Science was around at least by 3285 BCE.

Now this may be a bit of a stretch since the Egyptians added lots of religious and cultic notions around their science. Data Science was clouded in the occult and superstition until the experimental, scientific revolution that began in the 15^th century (or so). Galileo is often cited as the first experimenter that concerned himself with data accuracy and precision in an objective way around 1600 AD. It was the hard sciences of chemistry, physics and astronomy that pushed forward scientific data analysis. Later, botany, genetics and agronomy pushed data analysis into areas where understanding random, experimental “error” became critical.

Over time, it became clear that statistical analysis of experimental data was crucial for understanding the usefulness of the data. Initially this as attributed to unavoidable errors or limitations in measurement technologies. By the early 20^th century, however, it had become clear that all systems displayed probabilistic behavior to a greater or lesser degree. This was especially true with sub-microscopic phenomena (e.g. photons, electrons, etc.) and complex biological phenomena (e.g. mutation/variations, psychological trends and preferences, etc.).

We will return to these probabilistic issues in a later blog, but for now, be aware that the mathematics of data management – especially statistics – was a well-developed field long before we were talking about Big Data. Data Science has borrowed much of its analytical approach from the physical, biological and social sciences.

Data Science is essentially the:

• Collection of data,
• The categorizing of these data,
• The creation of predictive models from the categorized data and
• The application of the predictive models for useful purposes.

The various buzz words tend to focus on some aspect of this process and emphasizes specific tools.

Some of the Buzz Words:

Two closely related terms, Big Data and Data Mining began to emerge around 2000 and by 2010 had become the hot items in IT. The growth of data storage capabilities during the 1990’s made possible accessing terabytes of data. By the early 2000’s developments in parallel processing and large, volatile memory chips had dramatically increased the speed of data searching and handling. It became practical to investigate gigabytes of data simultaneously in fast, volatile memory rather than combing through multiple magnetic or optical discs. It became practical to do rapid statistical analysis on gigabytes of data hunting for potential relationships between data elements and including many simultaneous variables in linear and non-liner models.

By around 2010 the collection, storage, retrieval, investigation of gigabytes and terabytes of data became known as Big Data. The methods for collecting, storing, searching and retrieving relevant data from existing data became known as Data Mining. When we talk about Data Mining, we are focusing on the challenges of retrieving useful data from a variety of independent sources. Frequently, these sources are “legacy” databases created by old hardware and software. Sometimes these databases have little structure and are of questionable quality. The Data Mining expert deals with all these potential issues when trying to sort through giga- and terabytes of data.

Two other buzz words that are often used interchangeably are Statistics and Data Analytics. They are not the same. Statistics is a body of scientific, mathematical principles and theorems based on various types of data randomness. It first developed from studying games of chance (dice, cards, etc.), but became closely associated with the experimental sciences (especially agronomy, botany and chemistry).

Data Analytics uses Statistics extensively. Data and data trends are studied to see if “statistically valid relationships exist.” The question is, “Are there persistent relationships that cannot be explained simply by random chance?” The hope is that we can find relationships that are persistent enough to be used to predict the future. Ideally, the relationships are so persistent that they could be called “laws” that “cause” the results that we observe. We start by looking at “correlations” and then test them to see how “important” and persistent they are. In the hard sciences we can often run enough controlled experiments that we can start talking about “causality.” This is much less common in business and social sciences where we often are working “in the wild” or with “secondary” data – that is data we have “inherited” and not generated under conditions that we controlled.

Data Analytics is more than just statistical analysis, however. It includes evaluating the completeness and reliability of the data being used for statistical analysis. This has been one of the greatest challenges for Data Analysts. Historical data is never complete or without obvious and suspected errors. Data Analysts must decide what to do with such data without biasing the data set. There is constant tension between deciding which data are “good enough” and which data must be rejected as incomplete, inaccurate or irrelevant. The users of data should also be aware of the “judgements” that Data Analysts must make. Those “judgements” could make the analysis and conclusions biased or irrelevant.

Anyone who thinks that Data Science is purely objective does not understand it at all. The Data Scientist must “prejudge” many aspects. We can only hope that it does not lead to intention or even unintentional prejudice.

The goal of Data Science activities is to “learn” in the sense of building and validating models that allow the measurement of readily observable factors to predict important results. This notion is worth spending time pondering. When we “learn” we are creating “models” in our head that organize distinct observations and relate them to each other. We are hoping that A causes B with enough reliability that when we see A, we can get ready for B.

An example might be using data we have collected about buyer preferences. Assume we have done a survey of customer preferences that included satisfaction/dissatisfaction with how well our product lasted. Now suppose that our competition extended their warranty from ninety days to a year. We might wonder if we needed to extend our warranty program. We would be scrutinizing our data to see if it could be used to make a statistically valid model of how our customers will react to our competitor’s action. If we “knew” that, we might be able to react to minimize damage or even gain an advantage in the “exchange.” If we “knew” our customers were very satisfied with our product’s reliability, we might do something quite unexpected. We might run an ad campaign that emphasized our product longevity and make our competitor’s “need” for a warranty extension a negative in the minds of the consumer.

Modeling is rife with challenges, especially when all data are historical rather than experimental. Experiments are designed to control all factors that seem to be important and to randomly manipulate a few factors to see how results of interest are impacted. Good experimental design can often build a very strong case for causal relationships between a small number of very important factors and results. Historical data is rarely so nice and neat.

Historical data is collected as it happens. It frequently does not follow an experimental design nor is there much hope that all relevant factors were observed. There can be important factors that were not measured that “confound” the data. An example might be price and sales data that were taken during a period when the economy was slipping into a recession. Unless the analysis considered macro-economic factors, it could be useless. And finally, historical data is always plagued by errors and missing data points.

The famous statistician, Dr. George Box, was noted for saying, “All models are wrong, but some models are useful.” Models are useful, numerical approximation of reality if they can reliably predict important factors from easily measured ones. They may be useful for a season and pass away into history or they may indicate very persistent relationships that seem to always be reasonably true. Hence, the users of Models need to be careful to be neither too demanding of accuracy nor too certain of predictions. One of the arts in Modeling is for the Data Scientist to understand the data, the relationships and the data needs well enough to communicate to data users the reliability and limitations of any Model they create.

Pulling it all together – Artificial Intelligence (AI) and Machine Learning:

Perhaps the sexiest concepts in Data Science today are AI and Machine Learning. They are closely related concepts that either focus on the process of “thinking” (AI) or more on the gathering and using the “thinking” (Machine Learning). The concepts bring together much of what we have discussed so far. Let’s start with Machine Learning.

The clearest way to think about Machine Learning is to imagine an example – the Roomba. This amazing device uses Machine Learning to navigate your house and vacuum your floors. How does it do it?

The Roomba has multiple sensors for detecting when it runs into something and multiple pre-programmed actions to try to continue vacuuming. What it does differently from a “dumb machine” is that it remembers bumping into something and ties to map out a “better” performance next time. The Machine Learning part of Roomba is the sophisticated sensing equipment that is tied to a large memory and a fast, powerful processing system able to recall and use gigabytes of data. Roomba can “learn” to avoid obstacles by rote but optimizing the time and efficiency of vacuuming requires a bit more. This is where we tend to talk more about AI.

AI is focused on optimizing the algorithms for doing a task. It requires knowing about the task, guessing at reasonable optimizing actions, attempting many different actions, measuring results and then selecting and refining the decision algorithms that work the best. These algorithms include a decision tree for taking action and a Model to help select optimal solutions.

A simple algorithm might be, “If you can’t go forward, then try going right, repeat until you are going forward again.” A more sophisticated algorithm will look at many factors before just deciding to go right. Current position in the room and a history of what has happened in the past at that position would come into play. Furthermore, the outcomes of previous decisions made at that position would be considered.

AI isn’t very impressive if it is nothing more than remembering not to repeat mistakes. It becomes much more useful when algorithms are developed that successfully navigate “changes” to conditions. That is, when good decisions are made when a chair is arbitrary placed in the room. “Good” decisions generally follow from certain decision patterns. If we “learn” these patterns, incorporate them into the decision algorithm(s) and then modify them with the newest data, we are creating a much more powerful AI application.

Machine Learning and AI can also be illustrated in a data management project. Suppose we wish to use AI to set asking prices for houses we are selling. We would certainly want a large database of asking and selling prices for “relevant” houses. That could mean those in the neighborhood and of similar size but there could be a whole host of other features that could be relevant. Sadly, tastes, budgets, interest rates, consumer confidence and even our own financial goals are not static. If we could gather large chunks of data and measure how they interact to produce quick, profitable sales we might create an AI algorithm that would give us a distinct advantage.

We would use Machine Learning to go out and constantly get the “relevant data” and update our algorithms based on results we achieved in the marketplace. We would try many different optimizing algorithms to see which ones gave us the best result. Then we would have to try to find decision trees to integrate with our Models that effectively respond to the many changing features of the real estate business. Once we did that and verified that it produced improved decision making, we would be well on our way to having an AI based real estate business.

This all sounds easy enough until you look into the details. It is one thing to allow Roomba to stumble and fumble around learning how to vacuum a room, but a whole different matter to allow a new AI system to replace a real estate “expert” on setting house prices.

The history of AI and Machine Learning is full of spectacular failures. Nevertheless, there are many, less spectacular successes. We will talk about some of the failures and successes in future blogs. Like with any technology deployment, there are constraints and limitations that must be recognized and considered. We will talk about some of those in the next blog: What is Data Science? Part II

 

Stites & Associates, LLC (SALLC), is a technology development group founded by Ron Stites in 1996. The scientists and engineers at SALLC strive to help clients succeed at technology development and deployment by using Evidence-Based Decision-Making. Feel free to contact Ron Stites via email at [email protected].

 

Professor Stites?

We live in strange times, but hardly anything is stranger than me being called “Professor Stites.” Nevertheless, it happened.

I have just completed teaching the Spring Semester of MKT-310, “Marketing Research and Consumer Behavior” at Colorado Christian University (CCU) in Lakewood, Colorado. I have to say that it was one of the most rewarding experiences of my life. Of course, there’s a story here.

It began last fall with an email from the Dean of the School of Business and Leadership, Dr. Gary Ewen. Because of an illness, Dr. Ewen needed someone to teach “Marketing Research and Consumer Behavior” in the spring. The course was due for an update and this would require selecting a newer text and designing the course from scratch. Dr. Ewen had heard of me through a mutual friend and thought my background in general business, my previous experience teaching at a university level and my experience with business statistics would make me a pretty good fit. He assured me that he would be supportive in helping me through the many issues with teaching a revised class in a new environment. Honestly, I had long been hoping for such an opportunity and jumped at the chance.

The first task was to find a textbook that was reasonably up-to-date. The field of marketing research is in transition. Traditional marketing research has tended to be consumer behavior oriented, using focus groups and surveys to elucidate consumer preferences and buying habits. Texts that focus on these aspects tended to be psychology and social science oriented.

The newest trends in marketing research focus on the many different forms of digital marketing. That is, using digital communications to find, understand and influence buyer behavior. Digital marketing is the “hottest thing going.” Some texts seemed to go a bit overboard in their enthusiasm and read like a “Social Media Selling for Dummies.” It wasn’t easy to find a text that taught the fundamentals of marketing research while doing a reasonable job of recognizing the changing landscape.

I was able to find a newly updated text, the 8^th edition of Marketing Research by Dr. Alvin Burns, Dr. Ann Veeck and Dr. Ronald Bush. This new edition had added Dr. Veeck, as a co-author. Dr. Veeck added the new dimension of digital marketing research and data analytics. I had the great pleasure of communicating directly with Dr. Burns during the process of selecting the book and in the early design of the course. His help was instrumental to the overall success of the teaching effort.

Then came the really hard work – actually designing the course. This meant writing out the syllabus in detail, scheduling the lectures and tests, deciding between papers and projects and laying out the grading schedule. Dr. Ewen was extremely helpful in this area as were the many tools provided by authors of the text.

Perhaps the greatest contribution to the success of this class came from the students themselves. They definitely wanted to learn and were quite open about how things were going. They responded very positively to hands on projects and anything that seemed like practical knowledge.

The first project we did was with Craig Hospital. With the help of Lisa Stites (daughter-in-law) and her boss, Jake Manley, the students were able to review Craig’s marketing program and suggest improvements. This was such a success that we decided to modify the course schedule a bit to allow for a Final Project rather that a Final Exam.

The Final Project was a complete Marketing Research Project from beginning to end. The Research Question was:

How do CCU students perceive changes at CCU and how do those perceptions impact student retention?

The class organized and hosted a Focus Group, designed and administered an On-line Survey and performed a statistical evaluation of the data they collected using SPSS. The class developed a five-minute video on the Project and presented their findings to the President of the University and a group of Faculty members. Thirty percent of their final grade came from Administration and Faculty evaluation of the presentation. They received very high marks on clarity, thoroughness, persuasiveness and usefulness.

I have to admit that I learned a lot by teaching this class. I was especially struck by the similarities between the Marketing Research Process described by Dr. Burns and the more general process of Evidence-Based Decision-Making. We again see how careful collection and analysis of data results in good decisions unfettered by bias or hubris.

Naturally, the course can be improved, but all in all this class was a great success thanks to the help I received from many quarters. I would certainly do it again!

Ron Stites is the Founder and Principal at Stites & Associates, LLC, a group of technical professionals who work with clients to improve decision making in technology driven companies by applying good management judgment based on objective evidence and sound scientific thinking. Ron has served as an Adjunct Professor for Colorado Christian University. For more information see: www.tek-dev.net or email us at [email protected] .

The Monty Hall “Paradox”

Monty Hall hosted the game show “Let’s Make a Deal” for many years. One of the features of the show was to allow contestants to choose one of Door Number 1, 2 or 3 to win a big final prize. It might be a new car or a dream vacation, but it was much more valuable than the junk behind the other doors.

There grew a “paradox” around this selection process that went something like this:

Monty tells you that there is a brand new car behind Door Number 1, 2 or 3. Behind the other doors are goats. He asks you to pick one of the doors. You pick Door Number 1. To the horror of the sponsors, Monty decides to give you a hint. He knows the correct door, but he will not tell you that. Instead, he tells you that there is a goat behind Door Number 3. He gives you a chance to change your pick. Should you do it? Does it matter?

Most say that it doesn’t matter whether you change or not. They think that their odds of being correct have just gone up to 50%, but changing to Door Number 2 won’t improve those odds. Even Nobel laureates and statisticians tended to agree with this intuitive answer. They were wrong. You should ALWAYS change to Door Number 2 to increase your chance of success to 2/3!

Why?

This “problem” is often called a “paradox,” but it isn’t. The answer can be clearly demonstrated by computer simulations, physical experimentation and complex Bayesian probability. It is hard to “see” the answer without resorting to some form of demonstration.

Why is it so hard to “see”?

There are really three reasons:

1. We are loathe to think through the consequences of our first choice being wrong,
2. We are troubled by having to deal with the impact of new information, especially how it impacts the decision path we initially rejected and
3. It is hard for us to think about all of our choices resulting in probable outcomes and that our “best” solution is the one that “improves” our probability for “success” even though it does not make the outcome certain.

Because of these issues, we naturally gravitate to simple evaluations and fail to look deep enough into the possibilities to get to the right answer.

The right answer does NOT require a PhD in statistics. In fact, the most educated are often the most recalcitrant in their belief that the choice of Door Number 1 or 2 is of equal probability. Some have gone to great lengths to try to support their intuitive but incorrect conclusions. The solution is pretty simple, but the logic must be followed with care.

Event 1:

We pick Door Number 1. Our chance of being correct is 1 in 3 (or 1/3). Our chance of being wrong is 2 in 3 (or 2/3).

Event 2:

Monty looks at what is behind Doors Number 2 and 3 and tells us that behind one of them (in this case Door Number 3) is a goat. He gives us a chance to change our choice – obviously to Door Number 2.

Our logical choice will always be to switch to Door Number 2. That will double our chance of being correct from 1/3 to 2/3.

Event 2 does not change our probability of having chosen correctly the first time. It is still 1/3. What we do know, however, if our first choice was wrong and the car was behind Door Number 2 or 3, we now are CERTAIN that it is behind Door Number 2. This is known as assessing a conditional probability – that is figuring the chance of success after some other related event has occurred. The key to our understanding is assessing this second path, that is, the path where our first choice was wrong. It is this evaluation that give us the clue for what to do.

If we were wrong (and we will be wrong 2/3 of the time), then this new knowledge tells us with certainty that the car is behind Door Number 2. If we now switch to Door Number 2 our chances for picking correctly jump to 2/3.

Sometimes if we look at the options graphically it makes more sense:

 

 

What we see is that 2/3 of the time we will be going down the path we thought was wrong – that is the car was behind 2 or 3 and not 1. But, when Monty tells us that the car is NOT behind Door Number 3, we suddenly realize that IF we were wrong in our first guess, then the car MUST be behind Door Number 2. Since our first choice would be wrong 2/3 of the time, we can improve our chances of getting the car to 2 out of 3 times by changing our choice to Door Number 2. That is better than the 1 out of 3 we had at the outset. It is even better than some naïve guesses of 50/50 at Event 2.

If you don’t believe these results you are in good company. To prove it to yourself, simulate this with a friend using three playing cards. Try to pick one of the cards (maybe the Ace of Spades?), from among the other cards placed on the table face down. Have your friend look at the other two cards and tell you one of them that is NOT the card you are looking for (i.e. is NOT the Ace of Spades). In about 30 tries you will see that the analysis approximately matches your experience.

The moral of the story is that we often fail to evaluate new data correctly, especially when it appears to be contrary to or disconnected from our original hypotheses.

This is just another difficulty in staying on the path of Evidence-Based Decision-Making.

Stites & Associates, LLC, is a group of technical professionals who work with clients to evaluate, improve and deploy new technologies by applying Evidence-Based Decision-Making. The Founder, Ron Stites holds a BS in Chemistry and an MBA in Finance and Accounting. He teaches Business and Marketing Research at Colorado Christian University. For more information see: www.tek-dev.net.