Tag Archives: experiments

Understanding Design of Experiments (DoE) in Protein Purification

This webcast, from GE Life Sciences, seeks to provide an understanding Design of Experiments (DoE) using an example of protein purification. It begins with a good overview of the reason why multi-factorial experiments must be used while changing multiple factors at the same time in order to see interactions between factors. These interactions are completely missed by one-factor-at-a-time experiments.

While it is a good introduction it might be a bit confusing if you are not familiar with multi-factorial designed experiments. You may want to read some of the links below or take advantage of the ability to pause the video to think about what he says or to replay portions you don’t pick up immediately.

I have discussed the value of design of experiments in multiple posts on this blog in the past, including: Introductory Videos on Using Design of Experiments to Improve Results by Stu Hunter, Design of Experiments: The Process of Discovery is Iterative and Factorial Designed Experiment Aim.

He also provides a good overview of 3 basic aims of multivariate experiment (DoE):

  • screening (to determine which factors have the largest impact on the results that are most important)
  • optimization (optimize the results)
  • robustness testing (determine if there are risks in variations to factors)

Normally an experiment will focus on one of these aims. So you don’t know the most important factors you may choose to do a screening experiment to figure out which factors you want to study in detail in an optimization experiment.

It could be an optimized set of values for factors provides very good results but is not robust. If you don’t have easy way to make sure the factors do not vary it may be worthwhile to choose another option that provides nearly as good results but is much more robust (good results even with more variation within the values of the factors).

Related: YouTube Uses Multivariate Experiment To Improve Sign-ups 15% (2009)Combinatorial Testing for Software (2009)Marketers Are Embracing Statistical Design of Experiments (2005)

Transform the Management System by Experimenting, Iterating and Adopting Standard Work

In this short video, Dr. John Toussaint describes how ThedaCare applied leadership standard work to create a successful management transformation. The changes to the management system were tested by applying standard work for all positions in 2 parts of the organization (including all senior management positions) and learning and adapting and then spreading the new methods to the rest of the organization.

Changes to the management system require the same testing and piloting of changes on a small scale as other process changes. Experiment by going an inch wide and a mile deep, iterate over PDSA cycles, and once we have a solution that works adopt it widely (the A in PDSA).

Related: Systemic Workplace ExperimentsTransforming a Management System, A Case Study From the Madison Wisconsin Police DepartmentTransformation and Redesign at the White House Communications AgencyCulture Change Requires That Leaders Change Their BehaviorStandard Work InstructionsHow To Create a Continual Improvement Culture

George Box Webcast on Statistical Design in Quality Improvement

George Box lecture on Statistical Design in Quality Improvement at the Second International Tampere Conference in Statistics, University of Tampere, Finland (1987).

Early on he shows a graph showing the problems with American cars steady over a 10 years period. Then he overlays the results for Japanese cars which show a steady and significant decline of the same period.

Those who didn’t get to see presentations before power point also get a chance to see old school, hand drawn, overhead slides.

He discusses how to improve the pace of improvement. To start with informative events (events we can learn from) have to be brought to the attention of informed observers. Otherwise only when those events happen to catch the attention of the right observer will we capture knowledge we can use to improve. This results in slow improvement.

A control chart is an example of highlighting that something worth studying happened. The chart will indicate when to pay attention. And we can then improve the pace of improvement.

Next we want to encourage directed experimentation. We intentionally induce informative events and pay close attention while doing so in order to learn.

Every process generates information that can be used to improve it.

He emphasis the point that this isn’t about only manufacturing but it true of any process (drafting, invoicing, computer service, checking into a hospital, booking an airline ticket etc.).

He then discussed an example from a class my father taught and where the students all when to a TV plant outside Chicago to visit. The plant had been run by Motorola. It was sold to a Japanese company that found there was a 146% defect rate (which meant most TVs were taken off the line to be fixed at least once and many twice) – this is just the defect rate before then even get off the line. After 5 years the same plant, with the same American workers but a Japanese management system had reduced the defect rate to 2%. Everyone, including managers, were from the USA they were just using quality improvement methods. We may forget now, but one of the many objections managers gave for why quality improvement wouldn’t work in their company was due to their bad workers (it might work in Japan but not here).

He references how Deming’s 14 points will get management to allow quality improvement to be done by the workforce. Because without management support quality improvement processes can’t be used.

With experimentation we are looking to find clues for what to experiment with next. Experimentation is an iterative process. This is very much the mindset of fast iteration and minimal viable product (say minimal viable experimentation as voiced in 1987).

There is great value in creating iterative processes with fast feedback to those attempting to design and improve. Box and Deming (with rapid turns of the PDSA cycle) and others promoted this 20, 30 and 40 years ago and now we get the same ideas tweaked for startups. The lean startup stuff is as closely related to Box’s ideas of experimentation as an iterative process as it is to anything else.

Related: Ishikawa’s seven quality control tools

He also provided a bit of history that I was not aware of saying the first application of orthogonal arrays (fractional factorial designs) in industry was by Tippett in 1933. And he then mentioned work by Finney in 1945, Plackett and Burman in 1946 and Rao in 1947.

Interview on PDSA, Deming, Strategy and More

Bill Fox interviewed me and has posted part one of the interview on his web site: Predicting Results in the Planning Stage (sorry, the link has been hijacked to forward to an unrelated page [so obviously I removed the link], I have posted the interview which can now be reached here):

Bill: John, what is your best process improvement strategy or tactic that has worked well for you or your clients?

John: I would say the PDSA improvement cycle and a few key practices in using the PDSA properly like predicting the results in the plan stage—something that a lot of the times people do not do—to determine what would be done based on the results of that prediction.

People discover, especially when they’re new to this stuff, regarding the data that they’re collecting, that maybe even if they got the results they are predicting, they still don’t have enough data to take action. So you figure that even if that number is 30, they would need to know three other things before they make the change. So then, in the plan stage, you can figure that you need to address these other issues, too. At any time that people are collecting data is useful to figure out, for instance: “What do we need to do if the result is 30 or if the result is 3?” And if you don’t have any difference, why are you collecting the data?

Another important piece is the D in Plan, Do, Study, Act. It means “do the experiment”. A lot of times, people get confused into thinking that D means deploy the results or something like that, but thinking of D as ‘doing the experiment’ can be helpful.

A really big key between people that use PDSA successfully and those who don’t is that the ones that do it successfully turn the cycle quickly.

Another response:

Bill: What is the biggest misunderstanding about the Deming Management System you think people have?

John: I would say that there are a couple. The followers that want to pin everything to Deming tend to overlook the complexities and nuances and other things.

The other problem is that some of the critics latch on to a specific quote from Deming, something like a one-sentence long quote, and then they extrapolate from that one sentence-long quote what that means. And the problem is that Deming has lots of these one-sentence quotes that are very memorable and meaningful and useful, but they don’t capture every nuance and they don’t alone capture what it really means (you need to have the background knowledge to understand it completely).

They are sort of trying to oversimplify the message into these sound bites, and I find that frustrating. Because those individual quotes are wonderful, but they are limited to one little quote out of hours of videotape, books, articles, and when you don’t understand the context in which that resides, that’s a problem.

See the full interview for more details and other topics. I think it is worth reading, of course I am a bit biased.

Related: more interviews with John HunterInterviews with John Hunter on his book: Management MattersDeming and Software DevelopmentLean Blog Podcast with John Hunter

Taking Risks Based on Evidence

My opinion has long been that football teams are too scared to take an action that is smart but opens the coach to criticism. So instead of attempting to make it on 4th down (if you don’t understand American football, just skip this post) they punt because that is the decision that is accepted as reasonable.

So instead of doing what is wise they do what avoids criticism. Fear drives them to take the less advantageous action. Now I have never looked hard at the numbers, but my impression is that it is well worth the risk to go for it on 4th down often. In a quick search I don’t see a paper by a Harvard professor (this article refers to it also – Fourth down: To punt or to go?) on going for it on 4th down but I found on by a University of California, Berkeley economist (David Romer wrote called “Do Firms Maximize? Evidence from Professional Football.”).

On the 1,604 fourth downs in the sample for which the analysis implies that teams are on average better off kicking, they went for it only nine times. But on the 1,068 fourth downs for which the analysis implies that teams are on average better off going for it, they kicked 959 times.

My guess is that the advantages to going for it on 4th down are greater for high school than college which is greater than the advantage for the pros (but I may be wrong). My guess is this difference is greater the more yardage is needed. Basically my feeling is the variation in high school is very high in high school and decreases with greater skill, experience and preparation. Also the kicking ability (punting and field goals) impacts the choices of going for it on 4th down and that dramatically increases in college. So if I am correct, I think pro coaches should be more aggressive on 4th down, but likely less aggressive than high school coaches should be.

But in any event the data should be explored and strategies should be tested.

Continue reading

Resources for Using the PDSA Cycle to Improve Results

graphic image showing the PDSA cycle

PDSA Improvement cycle graphic from my book – Management Matters

Using the PDSA cycle (plan-do-study-act) well is critical to building a effective management system. This post provides some resources to help use the improvement cycle well.

I have several posts on this blog about using the PDSA cycle to improve results including:

The authors and consultants with Associates for Process Improvement have the greatest collection of useful writing on the topic. They wrote two indispensable books on the process improvement through experimentation: The Improvement Guide and Quality Improvement Through Planned Experimentation. And they have written numerous excellent articles, including:

Related: Good Process Improvement PracticesThe Art of Discovery (George Box)Planning requires prediction. Prediction requires a theory. (Ron Moen)

Jeff Bezos: Innovation, Experiments and Long Term Thinking

Jeff Bezos, bought the Washington Post. He has long showed a willingness to take a long term view at Amazon. He is taking that same thinking to the Washington Post:

In my experience, the way invention, innovation and change happen is [through] team effort. There’s no lone genius who figures it all out and sends down the magic formula. You study, you debate, you brainstorm and the answers start to emerge. It takes time. Nothing happens quickly in this mode. You develop theories and hypotheses, but you don’t know if readers will respond. You do as many experiments as rapidly as possible. ‘Quickly’ in my mind would be years.”

The newspaper business is certainly a tough one today – one that doesn’t seem to have a business model that is working well (for large, national papers). I figured the answer might be that a few (of the caliber of Washington Post, New York Times…) would be owed by foundations and supported largely by a few wealthy people that believed in the value of a strong free press and journalism. Maybe Bezos will find a business model that works. Or maybe he will just run it essentially as a foundation without needing a market return on his investment.

The Guardian (where the article with the quote was published) is an example of good journalism by a foundation. ProPublica is another (though I guess it is really a non-profit but most of the funding seems to be via foundations).

Related: Jeff Bezos and Root Cause Analysis (2009)Amazon Innovation (2006)Jeff Bezos on Lean Thinking (2005)Jeff Bezos Spends a Week Working in Amazon’s Kentucky Distribution Center (2009)

Design of Experiments: The Process of Discovery is Iterative

This video is another excerpt on the design of experiments videos by George Box, see previous posts: Introduction to Fractional Factorial Designed Experiments and The Art of Discovery. This video looks at learning about experimental design using paper helicopters (the paper linked there may be of interest to you also).

In this example a screening experiment was done first to find those factors that have the largest impact on results. Once the most important factors are determined more care can be put into studying those factors in greater detail.

The video was posted by Wiley (with the permission of George’s family), Wiley is the publisher of George’s recent autobiography, An Accidental Statistician, and many of his other books.

The importance of keeping the scope (in dollars and time) of initial experiments down was emphasized in the video.

George Box: “Always remember the process of discovery is iterative. The results of each stage of investigation generating new questions to answered during the next.”

Soren Bisgaard and Conrad Fung also appear in this except of the video.

The end of the video includes several suggested resources including: Statistics for Experimenters, Out of the Crisis and The Scientific Context of Quality Improvement.

Related: Introductory Videos on Using Design of Experiments to Improve Results (with Stu Hunter)Why Use Designed Factorial Experiments?brainstormingWhat Can You Find Out From 12 Experimental Runs?

What is the Explanation Going to be if This Attempt Fails?

Occasionally during my career I have been surprised by new insights. One of the things I found remarkable was how quickly I thought up a new explanation for what could have caused a problem when the previously expressed explanation was proven wrong. After awhile I stopped finding it remarkable and found it remarkable how long it took me to figure out that this happened.

I discovered this as I programmed software applications. You constantly have code fail to run as you expect and so get plenty of instances to learn the behavior I described above. While I probably added to my opportunities to learn by being a less than stellar coder I also learned that even stellar coders constantly have to iterate through the process of creating code and seeing if it works, figuring out why it didn’t and trying again.

The remarkable thing is how easily I could come up with an new explanation. Often nearly immediately upon what I expected to work failing to do so. And one of the wonderful things about software code is often you can then make the change in 10 minutes and a few minutes later see if it worked (I am guessing my brain kept puzzling over the ideas involved and was ready with a new idea when I was surprised by failure).

When I struggled a bit to find an initial explanation I found myself thinking, “this has to be it” often because of two self reinforcing factors.

First, I couldn’t think of anything else that would explain it. Sometimes you will think right away of 4 possible issues that could cause this problem. But, when I struggled to find any and then finally came up with an idea it feels like if there was another possibility I should have thought of it while struggling to figure out what I finally settled on.

Second, the idea often seems to explain exactly what happened, and it often feels like “of course it didn’t work, what was I thinking I need to do x.” This often turns out to be true, doing x solves the problem and you move on. But a remarkable percentage of the time, say even just 10%, it doesn’t. And then I would find myself almost immediately thinking, of course I need to do y. Even when 10 seconds ago I was convinced there was no other possibility.

Continue reading

The Art of Discovery

Quality and The Art of Discovery by Professor George Box (1990):


Quotes by George Box in the video:

“I think of statistical methods as the use of science to make sense of numbers”

“The scientific method is how we increase the rate at which we find things out.”

“I think the quality revolution is nothing more, or less, than the dramatic expansion of the of scientific problem solving using informed observation and directed experimentation to find out more about the process, the product and the customer.”

“It really amounts to this, if you know more about what it is you are doing then you can do it better and you can do it cheaper.”

“We are talking about involving the whole workforce in the use of the scientific method and retraining our engineers and scientists in a more efficient way to run experiments.”

“Tapping into resources:

  1. Every operating system generates information that can be used to improve it.
  2. Everyone has creativity.
  3. Designed experiments can greatly increase the efficiency of experimentation.

An informed observer and directed experimentation are necessary for the scientific method to be applied. He notes that the control chart is used to notify an informed observer to explain what is special about the conditions when a result falls outside the control limits. When the chart indicates a special cause is likely present (something not part of the normal system) an informed observer should think about what special cause could lead to the result that was measured. And it is important this is done quickly as the ability of the knowledgable observer to determine what is special is much greater the closer in time to the result was created.

The video was posted by Wiley (with the permission of George’s family), Wiley is the publisher of George’s recent autobiography, An Accidental Statistician: The Life and Memories of George E. P. Box, and many of his other books.

Related: Two resources, largely untapped in American organizations, are potential information and employee creativityStatistics for Experimenters (book on directed experimentation by Box, Hunter and Hunter)Highlights from 2009 George Box SpeechIntroductory Videos on Using Design of Experiments to Improve Results (with Stu Hunter)

Introductory Videos on Using Design of Experiments to Improve Results

The video shows Stu Hunter discussing design of experiments in 1966. It might be a bit slow going at first but the full set of videos really does give you a quick overview of the many important aspects of design of experiments including factorial designed experiments, fractional factorial design, blocking and response surface design. It really is quite good, if you find the start too slow for you skip down to the second video and watch it.

My guess is, for those unfamiliar with even the most cursory understanding of design of experiments, the discussion may start moving faster than you can absorb the information. One of the great things about video is you can just pause and give yourself a chance to catch up or repeat a part that you didn’t quite understand. You can also take a look at articles on design of experiments.

I believe design of experiments is an extremely powerful methodology of improvement that is greatly underutilized. Six sigma is the only management improvement program that emphasizes factorial designed experiments.

Related: One factor at a time (OFAT) Versus Factorial DesignsThe purpose of Factorial Designed Experiments

Continue reading

Richard Feynman Explains the PDSA Cycle

Ok, really Richard Feynman Explains the scientific method. But his thoughts make the similarity between the PDSA cycle and the scientific method obvious.

1) Plan, hypothesis.
You make a guess about a theory (in using the PDSA cycle this step is often missed, while in the scientific method this is of the highest priority). You make a prediction based on that theory.

2) Do the experiment

3) Study the results

If the results disprove the theory you were wrong. If they results don’t disprove the theory you may have a useful theory (it can also be that your theory is still wrong, but this experiment happened not to provide results that disprove it).

Step 4, Act, only exists for PDSA. In science the aim is to learn and confirm laws. While the PDSA cycle has an aim to learn and adopt methods that achieve the desired results.

Richard Feynman: “If it disagrees with experiment it is wrong, in that simple statement is the key to science, it doesn’t make any difference how beautiful your guess is, it doesn’t make a difference how smart you are (who made the guess), or what his name is, if it disagrees with experiment it is wrong.”

Actually far to often “PDSA” fails to adopt this understanding. Instead it become PA: no study of the results, just implement and we all already agree it is going to work so don’t bother wasting time testing that it actually does. Some organization do remember to study results of the pilot experiments but then forget to study the results when the new ideas are adopted on a broader scale.

Related: Does the Data Deluge Make the Scientific Method Obsolete?Video of Young Richard Feynman Talking About Scientific ThinkingHow to Use of the PDSA Improvement Cycle Most EffectivelyUsing Design of Experiments

Keys to the Effective Use of the PDSA Improvement Cycle

The PDSA improvement cycle was created by Walter Shewhart where Dr. Deming learned about it. An improvement process is now part of many management improvement methods (A3 for lean manufacturing, DMAIC for six sigma and many other modifications). They are fairly similar in many ways. The PDSA cycle (Plan, Do, Study, Act) has a few key pieces that are either absent in most others processes of greatly de-emphasized which is why I prefer it (A3 is my second favorite).

The PDSA cycle is a learning cycle based on experiments. When using the PDSA cycle prediction of the results are important. This is important for several reasons but most notably due to an understanding of the theory of knowledge. We will learn much more if we write down our prediction. Otherwise we often just think (after the fact); yeah that is pretty much what I expected (even if it wasn’t). Also we often fail to think specifically enough at the start to even have a prediction. Forcing yourself to make a prediction gets you to think more carefully up front and can help you set better experiments.

An organization using PDSA well will turn the PDSA cycle several times on any topic and do so quickly. In a 3 month period turning it 5 times might be good. Often those organizations that struggle will only turn it once (if they are lucky and even reach the study stage). The biggest reason for effective PDSA cycles taking a bit longer is wanting more data than 2 weeks provides. Still it is better to turn it several times will less data – allowing yourself to learn and adjust than taking one long turn.

The plan stage may well take 80% (or even more) of the effort on the first turn of the PDSA cycle in a new series. The Do stage may well take 80% of of the time – it usually doesn’t take much effort (to just collect a bit of extra data) but it may take time for that data to be ready to collect. In the 2nd, 3rd… turns of the PDSA cycle the Plan stage often takes very little time. Basically you are just adjusting a bit from the first time and then moving forward to gather more data. Occasionally you may learn you missed some very important ideas up front; then the plan stage may again take some time (normally if you radically change your plans).

Remember to think of Do as doing-the-experiment. If you are “doing” a bunch of work (not running an experiment and collecting data) that probably isn’t “do” in the PDSA sense.

Study should not take much time. The plan should have already have laid out what data is important and an expectation of what results will be achieved and provide a good idea on next steps. Only if you are surprised (or in the not very common case that you really have no idea what should come next until you experiment) will the study phase take long.

Continue reading

Factorial Designed Experiment Aim

Multivariate experiments are a very powerful management tool to learn and improve performance. Experiments in general, and designed factorial experiments in particular, are dramatically underused by managers. A question on LinkedIn asks?

When doing a DOE we select factors with levels to induce purposely changes in the response variable. Do we want the response variable to move within the specs of the customers? Or it doesn’t matter since we are learning about the process?

The aim needs to consider what you are trying to learn, costs and potential rewards. Weighing the various factors will determine if you want to aim to keep results within specification or can try options that are likely to return results that are outside of specs.

If the effort was looking for breakthrough improvement and costs of running experiments that might produce results outside of spec were low then specs wouldn’t matter much. If the costs of running experiments are very high (compared with expectations of results) then you may well want to try designed experiment values that you anticipate will still produce results within specs.

There are various ways costs come into play. Here I am mainly looking at the costs as (costs – revenue). For example the case where if the results are withing spec and can be used the costs (net costs, including revenue) of the experiment run are substantially lower.
Continue reading

Learn by Seeking Knowledge – Not Just from Mistakes

Being open to new ideas and new knowledge is what is needed to learn. Experimenting, seeking out new knowledge is even better.

You can be successful and see an even better way to do things and learn from it. This seems the best way to learn to me – not to just learn from mistakes. Of course this means your goal has to be improvement not just avoiding more mistakes than before.

Your actions are based on theories (often unconsciously): and learning involves improving those theories. Learning requires updating faulty ideas (or learning new ideas – in which case ignorance rather than a faulty theory may have lead to the mistake). Encouraging people to learn from mistakes is useful when it is about freeing them to make errors and learn from them. But you should be learning all the time – not just when you make mistakes.

You can be also be wrong and not learn (lots of people seem to do this). This is by far the biggest state I see. It isn’t an absence of people making mistakes (including carrying out processes based on faulty theories) that is slowing learning. People are very reluctant to make errors of commission (and errors of commission due to a change is avoided even more). This reluctance obviously makes learning (and improvement) more difficult. And the reluctance is often enhanced by fear created by the management system.

It is best to be open and seek out new knowledge and learn that way as much as possible. Now, you should also not be scared to be wrong. Taking the right risks is important to improving – encouraging creativity and innovation and risk taking is wise.

Experiment and be open to learn from what could be better and improve (PDSA is a great way to try things and evaluate how they work). And the idea is not to be so conservative that every turn of the PDSA cycle has no failures. In order to get significant successes it is likely you will try things that don’t always work.

The desire to improve understanding (and the desire to improve results provides focus to the learning) is what is valuable in learning – not being wrong. Creating a culture where being wrong needs to be avoided harms learning because people avoid risk and seek to distance themselves from failure instead of experimenting and digging into the details when something goes wrong. Instead of learning from mistakes people try to stay as far away from them and hide them from others. That is not helpful. But what is needed is more desire to continually learn – learning from mistakes is wise but hardly the only way to learn.

Related: The Illusion of Knowledgeconfirmation biasManagement is Prediction

Problems With Student Evaluations as Measures of Teacher Performance

Dr. Deming was, among other things a professor. He found the evaluation of professors by students an unimportant (and often counterproductive measure) – used in some places for awards and performance appraisal. He said for such a measure to be useful it should survey students 20 years later to see which professors made a difference to the students. Here is an interesting paper that explored some of these ideas. Does Professor Quality Matter? Evidence from Random Assignment of Students to Professors by Scott E. Carrell, University of California, Davis and National Bureau of Economic Research; and James E. West, U.S. Air Force Academy:

our results indicate that professors who excel at promoting contemporaneous student achievement, on average, harm the subsequent performance of their students in more advanced classes. Academic rank, teaching experience, and terminal degree status of professors are negatively correlated with contemporaneous value‐added but positively correlated with follow‐on course value‐added. Hence, students of less experienced instructors who do not possess a doctorate perform significantly better in the contemporaneous course but perform worse in the follow‐on related curriculum.

Student evaluations are positively correlated with contemporaneous professor value‐added and negatively correlated with follow‐on student achievement. That is, students appear to reward higher grades in the introductory course but punish professors who increase deep learning (introductory course professor value‐added in follow‐on courses). Since many U.S. colleges and universities use student evaluations as a measurement of teaching quality for academic promotion and tenure decisions, this latter finding draws into question the value and accuracy of this practice.

These findings have broad implications for how students should be assessed and teacher quality measured.

Related: Applying Lean Tools to University CoursesK-12 Educational ReformImproving Education with Deming’s IdeasLearning, Systems and ImprovementHow We Know What We Know

Extrinsic Incentives Kill Creativity

If you read this blog, you know I believe extrinsic motivation is a poor strategy. This TED webcast Dan Pink discusses studies showing extrinsic rewards failing. This is a great webcast, definitely worth 20 minutes of your time.

  • “you’ve got an incentive designed to sharpen thinking and accelerate creativity and it does just the opposite. It dulls thinking and blocks creativity… This has been replicated over and over and over again for nearly 40 years. These contingent motivators, if you do this then you get that, work in some circumstances but in a lot of tasks they actually either don’t work or, often, they do harm.”
  • there is a mismatch between what science knows and what business does
  • “This is a fact.”

What does Dan Pink recommend based on the research? Management should focus on providing workplaces where people have autonomy, mastery and purpose to build on intrinsic motivation.

via: Everything You Think about Pay for Performance Could Be Wrong

Related: Righter IncentivizationWhat’s the Value of a Big Bonus?Dangers of Extrinsic MotivationMotivate or Eliminate De-MotivationGreat Marissa Mayer Webcast on Google Innovation

YouTube Uses Multivariate Experiment To Improve Sign-ups 15%

Google does a great job of using statistical and engineering principles to improve. It is amazing how slow we are to adopt new ideas but because we are it provides big advantages to companies like Google that use concepts like design of experiments, experimenting quickly and often… while others don’t. Look Inside a 1,024 Recipe Multivariate Experiment

A few weeks ago, we ran one of the largest multivariate experiments ever: a 1,024 recipe experiment on 100% of our US-English homepage. Utilizing Google Website Optimizer, we made small changes to three sections on our homepage (see below), with the goal of increasing the number of people who signed up for an account. The results were impressive: the new page performed 15.7% better than the original, resulting in thousands more sign-ups and personalized views to the homepage every day.

While we could have hypothesized which elements result in greater conversions (for example, the color red is more eye-catching), multivariate testing reveals and proves the combinatorial impact of different configurations. Running tests like this also help guide our design process: instead of relying on our own ideas and intuition, you have a big part in steering us in the right direction. In fact, we plan on incorporating many of these elements in future evolutions of our homepage.

via: @hexawiseMy brother has created a software application to provide much better test coverage with far fewer tests using the same factorial designed experiments ideas my father worked with decades ago (and yet still far to few people use).

Related: Combinatorial Testing for SoftwareStatistics for ExperimentersGoogle’s Website Optimizer allows for multivariate testing of your website.Using Design of Experiments

Statistics for Experimenters in Spanish

book cover of Estadística para Investigadores

Statistics for Experimenters, second edition, by George E. P. Box, J. Stuart Hunter and William G. Hunter (my father) is now available in Spanish.

Read a bit more can find a bit more on the Spanish edition, in Spanish. Estadística para Investigadores Diseño, innovación y descubrimiento Segunda edición.

Statistics for Experimenters – Second Edition:

Catalyzing innovation, problem solving, and discovery, the Second Edition provides experimenters with the scientific and statistical tools needed to maximize the knowledge gained from research data, illustrating how these tools may best be utilized during all stages of the investigative process. The authors’ practical approach starts with a problem that needs to be solved and then examines the appropriate statistical methods of design and analysis.

* Graphical Analysis of Variance
* Computer Analysis of Complex Designs
* Simplification by transformation
* Hands-on experimentation using Response Service Methods
* Further development of robust product and process design using split plot arrangements and minimization of error transmission
* Introduction to Process Control, Forecasting and Time Series

Book available via Editorial Reverte

Related: Statistics for Experimenters ReviewCorrelation is Not CausationStatistics for Experimenters Dataposts on design of experiments

What’s the Value of a Big Bonus?

What’s the Value of a Big Bonus? by Dan Ariely

To look at this question, three colleagues and I conducted an experiment. We presented 87 participants with an array of tasks that demanded attention, memory, concentration and creativity. We asked them, for instance, to fit pieces of metal puzzle into a plastic frame, to play a memory game that required them to reproduce a string of numbers and to throw tennis balls at a target. We promised them payment if they performed the tasks exceptionally well. About a third of the subjects were told they’d be given a small bonus, another third were promised a medium-level bonus, and the last third could earn a high bonus.

So it turns out that social pressure has the same effect that money has. It motivates people, especially when the tasks at hand require only effort and no skill. But it can provide stress, too, and at some point that stress overwhelms the motivating influence.

When I recently presented these results to a group of banking executives, they assured me that their own work and that of their employees would not follow this pattern. (I pointed out that with the right research budget, and their participation, we could examine this assertion. They weren’t that interested.)

This is an interesting look at an effect of bonuses. We all know monetary bonuses can influence behavior. The problem is the type of behaviors that result. Huge bonuses, for example, create huge incentives to risk the future of the company for the chance at a huge bonus for the executive. Extrinsic motivation leads to many problems.

Problems with bonuses: Losses Covered Up to Protect Bonuses“Pay for Performance” is a Bad IdeaProblems with BonusesBook: Punished By Rewards: The Trouble With Gold Stars, Incentive Plans, A’s, Praise, and Other Bribes by Alfie Kohn – posts on executive pay