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Taxes per Person by Country

I think that the idea that data lies is false, and that such a notion is commonly held a sign of lazy intellect. You can present data in different ways to focus on different aspects of a system. And you can make faulty assumptions based on data you look at.

It is true someone can just provide false data, that is an issue you have to consider when drawing conclusions from data. But often people just don’t think about what the data is really saying. Most often when people say data lies they just were misled because they didn’t think about what the data actually showed. When you examine data provided by someone else you need to make sure you understand what it is actually saying and if they are trying to support their position you may be wise to be clear they are not misleading you with their presentation of the data.

Here is some data from Greg Mankiw’s Blog. He wants to make his point that the USA is taxed more on par with Europe than some believe because he want to reduce current taxes. So he shows that while taxes as a percent of economic activity is low in the USA taxes per person is comparable to Europe.

Taxes/GDP x GDP/Person = Taxes/Person

France .461 x 33,744 = $15,556

Germany .406 x 34,219 = $13,893

UK .390 x 35,165 = $13,714

US .282 x 46,443 = $13,097

Canada .334 x 38,290 = $12,789

Italy .426 x 29,290 = $12,478

Spain .373 x 29,527 = $11,014

Japan .274 x 32,817 = $8,992

The USA is the 2nd lowest for percent of GDP taxes 28.2% v 27.4% for Japan. But in taxes per person toward the middle of the pack. France which has 46% taxes/GDP totals $15,556 in tax per person compared to $13,097 for the USA. Both measures of taxes are useful to know, in my opinion. Neither lies. Both have merit in providing a understanding of the system (the economies of countries).

Related: Fooled by RandomnessSimpson’s ParadoxMistakes in Experimental Design and InterpretationGovernment Debt as Percentage of GDP 1990-2008 by CountryCommunicating with the Visual Display of DataIllusion of Explanatory Depth

Prophet Unheard: Dr. W. Edwards Deming – 1992

This is an interesting video on Deming and American management (by the BBC in 1992): Prophet Unheard. It includes some nice old footage of Deming in Japan. The importance of respect for people is clear and the video also touches on the idea the danger of relying on data (when you do not understand variation and that many important matters and unmeasurable). The video features many snippets of Dr. Deming speaking and includes Don Peterson, Ford CEO; Clare Crawford Mason, If Japan Can, Why Can’t We producer; and Myron Tribus.

Related: Dr. Deming Webcast on the 5 Deadly DiseasesRed Bead Experiment WebcastPerformance without Appraisalmanagement webcasts

Part two of the documentary explores the Deming Prize, understanding data and the PDSA cycle:
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The Biggest Manufacturing Countries in 2008 with Historical Data

Once again the USA was the leading country in manufacturing for 2008. And once again China grew their manufacturing output amazingly. In a change with recent trends Japan grew output significantly. Of course, the 2009 data is going to show the impact of a very severe worldwide recession.

Chart showing percent of output by top manufacturing countries from 1990 to 2008Chart showing the percentage output of top manufacturing countries from 1990-2008 by Curious Cat Management Blog, Creative Commons Attribution.

The first chart shows the USA’s share of the manufacturing output, of the countries that manufactured over $185 billion in 2008, at 28.1% in 1990, 27.7% in 1995, 32% in 2000, 28% in 2005, 28% in 2006, 26% in 2007 and 24% in 2008. China’s share has grown from 4% in 1990, 6% in 1995, 10% in 2000, 13% in 2005, 14% in 2006, 16% in 2007 to 18% in 2008. Japan’s share has fallen from 22% in 1990 to 14% in 2008. The USA has about 4.5% of the world population, China about 20%. See Curious Cat Investment blog post” Data on the Largest Manufacturing Countries in 2008.

Even with just this data, it is obvious the belief in a decades long steep decline in USA manufacturing is not in evidence. And, in fact the USA’s output has grown substantially over this period. It has just grown more slowly than that of China (as has every other country), and so while output in the USA has grown the percentage with China has shrunk. The percentage of manufacturing output by the USA (excluding output from China) was 29.3% in 1990 and 29.6% in 2008. The second chart shows manufacturing output over time.

charts showing the top manufacturing countries output from 1990-2008Chart showing the output of the top manufacturing countries from 1990-2008 by Curious Cat Management Blog, Creative Commons Attribution.

The 2008 China data is not provided for manufacturing alone (the latest UN Data, for global manufacturing, in billions of current USA dollars). The percentage of manufacturing (to manufacturing, mining and utilities) was 78% for 2005-2007 (I used 78% of the manufacturing, mining and utilities figure provided in the 2008 data). There is a good chance this overstates China manufacturing output in 2008 (due to very high commodity prices in 2008).

Hopefully these charts provide some evidence of what is really going on with global manufacturing and counteracts the hype, to some extent. Global economic data is not perfect. These figures are an attempt to capture the economic reality in the world but they are not a perfect proxy. This data is shown in 2008 USA dollars which is good in the sense that it shows all countries in the same light and we can compare the 1995 USA figure to 2005 without worrying about inflation. However foreign exchange fluctuations over time can show a country, for example, having a decline in manufacturing output in some year when in fact the output increased (just the decline against the USA dollar that year results in the data showing a decrease – which is accurate when measured in terms of USA dollars).

If the dollar declines substantially between when the 2008 data was calculated and the 2009 data is calculated that will give result in the data showing a substantial increase in those countries that had a currency strengthen against the USA dollar. At this time the Chinese Renminbi has not strengthened while most other currencies have – the Chinese government is retaining a peg to a specific exchange rate.

Korea (1.8% in 1990, 3% in 2008), Mexico (1.7% to 2.6%) and India (1.4% to 2.5%) were the only countries to increase their percentage of manufacturing output (other than China, of course, which grew from 3.9% to 18.5%).

Related: posts on manufacturingGlobal Manufacturing Data 2007Global Manufacturing Employment Data – 1979 to 2007Top 10 Manufacturing Countries 2006Top 10 Manufacturing Countries 2005lean manufacturing resources

Highlights from Recent George Box Speech

The JMP blog has posted some highlights from George Box’s presentation at Discovery 2009

Infusing his entire presentation with humor and fascinating tales of his memories, Box focused on sequential design of experiments. He attributed much of what he knows about DOE [design of experiments] to Ronald A. Fisher. Box explained that Fisher couldn’t find the things he was looking for in his data, “and he was right. Even if he had had the fastest available computer, he’d still be right,” said Box. Therefore, Fisher figured out how to study a number of factors at one time. And so, the beginnings of DOE.

Having worked and studied with many other famous statisticians and analytic thinkers, Box did not hesitate to share his characterizations of them. He told a story about Dr. Bill Hunter and how he required his students to run an experiment. Apparently a variety of subjects was studied [see 101 Ways to Design an Experiment, or Some Ideas About Teaching Design of Experiments]

According to Box, the difficulty of getting DOE to take root lies in the fact that these mathematicians “can’t really get the fact that it’s not about proving a theorem, it’s about being curious about things. There aren’t enough people who will apply [DOE] as a way of finding things out. But maybe with JMP, things will change that way.”

George Box is a great mind and great person who I have had the privilege of knowing my whole life. My father took his class at Princeton, then followed George to the University of Wisconsin-Madison (where Dr. Box founded the statistics department and Dad received the first PhD). They worked together building the UW statistics department, writing Statistics for Experimenters and founding the Center for Quality and Productivity Improvement among many other things.

Statistics for Experimenters: Design, Innovation, and Discovery shows that the goal of design of experiments is to learn and refine your experiment based on the knowledge you gain and experiment again. It is a process of discovery. If done properly it is very similar to the PDSA cycle with the application of statistical tools to aid in determining the impact of various factors under study.

Related: Box on QualityGeorge Box Quotationsposts on design of experimentsUsing Design of Experiments

Deming: There is No True Value

There is no true value of anything: data has meaning based on the operational definition used to calculate the data.

Walter Shewhart’s Statistical Method from the Viewpoint of Quality Control, forward by W. Edwards Deming:

There is no true value of anything. There is instead a figure that is produced by application of a master or ideal method of counting or measurement… no true value of the number of inhabitants within the boundaries of (e.g.) Detroit. A count of the number of inhabitants of Detroit is dependent upon the application of arbitrary rules for carrying out the count. Repetition of an experiment or of a count will exhibit variation.

Dr. Deming’s ideas on the theory of knowledge are the least understood and least seen in other management systems. The importance of understanding what data does, and does not tell you, is at least somewhat acknowledged in other management system but is often not found much in the actual practice of management. The execution often glosses over the importance of actually understanding statistics versus using formulas. Just using formulas is dangerous. It may be inconvenient but learning about the traps we can fall into in using data is important.

How often do you see the operational definition used to calculate the data you see with the data you are provided?

via: Shewhart, Deming and Data by Malcolm Chisholm

Related: How We Know What We KnowPragmatism and Management KnowledgeMeasuring and Managing Performance in OrganizationsDangers of Forgetting the Proxy Nature of Data

Statistical Learning as the Ultimate Agile Development Tool by Peter Norvig

Interesting lecture on Statistical Learning as the Ultimate Agile Development Tool by Peter Norvig. The webcast is likely to be of interest to a fairly small segment of readers of this blog. But for geeks it may be interesting. He looks at the advantages of machine learning versus hand programming every case (for example spelling correction).

Google translate does a very good job (for computer based translation) based on machine learning. You can translate any of the pages on this blog into over 30 languages using Google translate (using the widget in the right column).

Via: @seanstickle

Related: Mistakes in Experimental Design and InterpretationDoes the Data Deluge Make the Scientific Method Obsolete?Website DataAn Introduction to Deming’s Management Ideas by Peter Scholtes (webcast)

Communicating with the Visual Display of Data

graphs showing data sets with different looks even though some statistical characteristics are the same
Anscombe’s quartet: all four sets are identical when examined statistically, but vary considerably when graphed. Image via Wikipedia.

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Anscombe’s quartet comprises four datasets that have identical simple statistical properties, yet are revealed to be very different when inspected graphically. Each dataset consists of eleven (x,y) points. They were constructed in 1973 by the statistician F.J. Anscombe to demonstrate the importance of graphing data before analyzing it, and of the effect of outliers on the statistical properties of a dataset.

Of course we also have to be careful of drawing incorrect conclusions from visual displays.

For all four datasets:

Property Value
Mean of each x variable 9.0
Variance of each x variable 10.0
Mean of each y variable 7.5
Variance of each y variable 3.75
Correlation between each x and y variable 0.816
Linear regression line y = 3 + 0.5x

Edward Tufte uses the quartet to emphasize the importance of looking at one’s data before analyzing it in the first page of the first chapter of his book, The Visual Display of Quantitative Information.

Related: Great ChartsSimpson’s ParadoxSeeing Patterns Where None ExistsVisible DataControl ChartsEdward Tufte’s: Beautiful Evidence

Blame the Road – Not the Person

The system is responsible for 90, 92, 94, 97% of problems – W. Edwards Deming. Fix the system, don’t blame the people. When you seek system fixes you approach situations differently than if you search for people to blame.

By the way, I am often asked about the data supporting Deming’s contention that the system was responsible for 97% of the problems. This statement was not based on a set of data but on Dr. Deming’s decades of experience. And he increased the percentage over time – as he learned more.

Roads that are designed to kill

Half blamed the runner, saying she should not have been running in the street at that hour. Half blamed the driver, for not paying close enough attention. Not a single writer blamed the road.

Your streets are designed to kill people.

Vision Zero started about 30 years ago, when traffic safety researcher Claes Tingvall got the idea that we didn’t have to accept road traffic deaths as a fact of life. Tingvall and his colleagues said that these deaths were not “accidents’’ but were predictable and preventable. And they set out to prove it.

One of the ways they began to protect people was to put barriers down the center of two-lane roads. They showed that this could be done cheaply. When Mylar – a strong polyester film – is supported by closely spaced plastic poles, it can keep cars from crossing the median. When the Swedes used this type of center barrier to separate the traffic going in opposite directions, they effectively prevented head-on collisions and the death rate on these roads fell by 70 percent to 80 percent.

Global health research shows more improvements can save lives. For example, Ghana put in rumble strips – small bumps spaced closely together – across all the roads leading into the capital city of Accra, reducing fatalities by 35 percent. Research has shown that speed bumps on roads are one of the “best buys” in all of global health.

Most people think we are doing all that can be done to keep our roads safe. They are wrong. Road traffic injuries kill more than a million people a year worldwide, including 40,000 a year in the United States.

Is a situation killing 40,000 people in the USA a year a health care issue? It sure seems to me it would be. It probably isn’t a disease management issue though (some might try to say bad roads are a disease but I wouldn’t say that). I think this is one, of many examples, that shows that we have a disease and injury management system not a health care system (in addition to illustrating systems thinking, effective root cause analysis, PDSA, innovation, respect for people…).

Related: Find the Root Cause Instead of the Person to BlameTraffic Congestion and a Non-SolutionChecklists Save LivesSaving Lives: US Health Care ImprovementThe Economic Benefits of Walkable CommunitiesSWAT Raid Signs of Systemic FailuresSystem Improvement to Respond to the Dynamics of Crowd DisastersThe Leading Causes of Death

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

Don’t Hide Problems in Computers

Making things visible is a key to effective management. And data in computers can be easy to ignore. Don’t forget to make data visible. Paul Levy, CEO of Beth Israel Deaconess Medical Center in Boston recently hosted Hideshi Yokoi, president of the Toyota Production System Support Center and wrote this blog post:

Together, we visited gemba and observed several hospital processes in action, looking for ways to reduce waste and reorganize work. It was fascinating to have such experts here and see things through their eyes. Mr. Yokoi’s thoughts and observations are very, very clear, notwithstanding a command of English that is still a work in progress.

The highlight? At one point, we pointed out a new information system that we were thinking of putting into place to monitor and control the flow of certain inventory. Mr. Yokoi’s wise response, suggesting otherwise, was:

“When you put problem in computer, box hide answer. Problem must be visible!”

The mission of the Toyota Production System Support Center to share Toyota Production System know-how with North American organizations that have a true desire to learn and adopt TPS.

Related: The Importance of Making Problems VisibleGreat Visual Instruction ExampleHealth Care the Toyota Way

CEO’s Castles and Company Performance

Where are the Shareholders’ Mansions? CEOs’ Home Purchases, Stock Sales, and Subsequent Company Performance by Crocker H. Liu, Arizona State University and David Yermack, New York University – Stern School of Business

We study real estate purchases by major company CEOs, compiling a database of the principal residences of nearly every top executive in the Standard & Poor’s 500 index. When a CEO buys real estate, future company performance is inversely related to the CEO’s liquidation of company shares and options for financing the transaction. We also find that, regardless of the source of finance, future company performance deteriorates when CEOs acquire extremely large or costly mansions and estates. We therefore interpret large home acquisitions as signals of CEO entrenchment. Our research also provides useful insights for calibrating utility based models of executive compensation and for understanding patterns of Veblenian conspicuous consumption.

To understand better the reasons behind the underperformance of companies whose CEOs acquire very large homesteads, we read news stories about major events affecting the firms in our sample in which a CEO acquires a property with at least 10 acres or a 10,000 square foot house. These news stories suggest parallels between the CEOs’ oversight of their personal assets and management of their companies. No less than nine of the 25 CEOs attempted major corporate acquisitions in the two years following their personal acquisitions of very large real estate,9 and seven of the 25 announced significant capital investment initiatives involving the construction or expansion of corporate facilities. An additional two firms became mired in accounting scandals shortly after their CEOs purchased mansions, and one firm saw a previously agreed merger collapse.

Using a database of principal residences of company CEOs, we study whether these executives’ decisions about home ownership contain information useful for predicting the future path of their companies’ stock prices. We find that CEOs who acquire extremely large properties exhibit inferior ex post stock performance, a result consistent with large mansions and estates being proxies for CEO entrenchment. We also find that the method of financing a home’s acquisition is informative about future stock returns. A general pattern of CEO sales of their firms’ shares and options exists over the twelve months leading up to the date of home acquisition. However, when the CEO does not sell any shares, his stock performs significantly better ex post than the stocks of firms whose CEOs do liquidate equity to finance their houses. The retention of company shares simultaneous with a new home purchase, despite the presence of an evident personal liquidity need, appears to send a signal of commitment by a CEO to his company.

That we put in power CEO’s that see themselves as nobility with the right to build castles (and many of these CEO castles dwarf all but the most conspicuous castle built by nobility) by taking the wealth produced by others from corporate coffers is a sign of our failure to select acceptable leaders for companies.

Related: Another Year of CEO’s Taking Hugely Excessive PayExcessive Executive PayExposing CEO Pay ExcessesNarcissistic Cadre of Senior Executives9 Deadly Diseases

When Performance-related Pay Backfires

When Economic Incentives Backfire by Samuel Bowles, Sante Fe Institute

Dozens of recent experiments show that rewarding self-interest with Economic incentives can backfire when they undermine what Adam Smith called “the moral sentiments.”

Punished by Rewards, by Alfie Kohn, is a great book on this topic. The area of “motivating” employees is one it is often hard for managers to learn. Even managers that have been studying Deming, Ackoff, Ohno… for years still have trouble with the idea that trying to find the right incentive scheme to motivate the right behavior is the wrong approach. Read the The Human Side Of Enterprise by Douglas Mcgregor (in 1960) to re-enforce the understanding of human motivation provided by Toyota’s respect for people principles.

Managers need to eliminate de-motivation in the work systems not try and find bonus schemes to motivate behavior. Eliminating de-motivation is often much more work. You can’t just get some money from the bonus pool and start giving it away. You have to manage. But if you are a manager you shouldn’t be afraid to actually manage the system and make it better.

Related: “Pay for Performance” is a Bad IdeaReward and Incentive Programs are Ineffective — Even Harmful by Peter Scholtes – The Defect Black MarketWhat’s the Value of a Big Bonus?Problems with BonusesLosses Covered Up to Protect BonusesStop Demotivating Employees

When performance-related pay backfires:
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Google’s Innovative Use of Economics

Secret of Googlenomics: Data-Fueled Recipe Brews Profitability

Google depends on economic principles to hone what has become the search engine of choice for more than 60 percent of all Internet surfers, and the company uses auction theory to grease the skids of its own operations. All these calculations require an army of math geeks, algorithms of Ramanujanian complexity, and a sales force more comfortable with whiteboard markers than fairway irons.

Varian tried to understand the process better by applying game theory. “I think I was the first person to do that,” he says. After just a few weeks at Google, he went back to Schmidt. “It’s amazing!” Varian said. “You’ve managed to design an auction perfectly.” To Schmidt, who had been at Google barely a year, this was an incredible relief. “Remember, this was when the company had 200 employees and no cash,” he says. “All of a sudden we realized we were in the auction business.”

Google even uses auctions for internal operations, like allocating servers among its various business units. Since moving a product’s storage and computation to a new data center is disruptive, engineers often put it off. “I suggested we run an auction similar to what the airlines do when they oversell a flight. They keep offering bigger vouchers until enough customers give up their seats,” Varian says. “In our case, we offer more machines in exchange for moving to new servers. One group might do it for 50 new ones, another for 100, and another won’t move unless we give them 300. So we give them to the lowest bidder—they get their extra capacity, and we get computation shifted to the new data center.”

Google continues to make bold moves putting faith in their ability to find innovative solutions that others reject as impossible. It is a challenging but interesting path to success, for them, at least.

Related: Google Should Stay True to Their Management PracticesGoogle’s Answer to Filling Jobs Is an AlgorithmThe Google Way: Give Engineers RoomGoogle Website OptimizerGoogle: Experiment Quickly and Oftenposts on innovation in management

Revealed Preference

Revealed Preference: the preference consumers display by their action, in contrast to what they may say they prefer. While surveys may be useful people often say they will do one thing and actually when given the choice to do so, don’t.

Normally what matters is not what people say they want but what they actually will choose. For that reason revealed preference is a better measure than stated preference. Stated preference is often used as a proxy for actual preference (which may be fine) but it is important to understand that it is just a proxy for actual preference.

See more explanations from the Curious Cat Management Dictionary.

Related: Packaging ImprovementAll Models Are Wrong But Some Are UsefulDangers of Forgetting the Proxy Nature of DataConfirmation BiasBe Careful What You Measure

Why Setting Goals can Backfire

Dr. Deming long ago stated in his 14 obligations of management: “Eliminate numerical goals, numerical quotas and management by objectives.” I think he was right then, and is right now. A goal can help set the scope of the effort. If you are aiming for 2% improvement different strategies may make sense than if you are aiming at 50% improvement. But that type of use is rare. The problem with goals is what actually happens in organizations. They create serious systemic problems and should be avoided (other than in setting the scope). They are deeply ingrained in the way many people think, but we would be better if we could eliminate the use of goals, as they are used now (mainly as arbitrary numerical goals).

Ready, Aim … Fail, Why setting goals can backfire

In clawing toward its number, GM offered deep discounts and no-interest car loans. The energy and time that might have been applied to the longer-term problem of designing better cars went instead toward selling more of its generally unloved vehicles. As a result, GM was less prepared for the future, and made less money on the cars it did sell. In other words, the world’s largest car company – a title it lost to Toyota last year – fell victim to a goal.

Rather than reflexively relying on goals, argues Max Bazerman, a Harvard Business School professor and the fourth coauthor of “Goals Gone Wild,” we might also be better off creating workplaces and schools that foster our own inherent interest in the work. “There are lots of organizations where people want to do well, and they don’t need those goals,” he says. Bazerman and others hold up Google as an example of a company that manages to do this, in part by explicitly setting aside time for employees to pursue their own projects and interests.

Today, as the economic situation upends millions of lives, it is also forcing the reexamination of millions of goals – not only the revenue targets of battered firms, but the career aims of workers and students, and even the ambitions of the newly installed administration. And while it never feels good to give up on a goal, it may be a good time to ask which of the goals we had set for ourselves were things we really needed to achieve, and which were things we only thought we should – and what the difference has been costing us.

Related: Measuring and Managing Performance in OrganizationsArbitrary Rules Don’t WorkThe Defect Black MarketGoals can Distract from ImprovementBe Careful What You Measure

Red Bead Experiment Webcast

Dr. Deming used the red bead experiment to present a view into management practices and his management philosophy. The experiment provides insight into all four aspects of Dr. Deming’s management system: understanding variation, understanding psychology, systems thinking and the theory of knowledge.

Red Bead Experiment by Steve Prevette

Various techniques are used to ensure a quality (no red bead) product. There are quality control inspectors, feedback to the workers, merit pay for superior performance, performance appraisals, procedure compliance, posters and quality programs. The foreman, quality control, and the workers all put forth their best efforts to produce a quality product. The experiment allows the demonstration of the effectiveness (or ineffectiveness) of the various methods.

Related: Fooled by RandomnessPerformance Measures and Statistics CoursePerformance without AppraisalExploring Deming’s Management IdeasEliminate Slogans

How to Create a Control Chart for Seasonal or Trending Data

Lynda Finn, President of Statistical Insight, has written an article on how to create a control chart for seasonal or trending data (where there is an underlying structural variation in the data). Essentially you need to account for the structural variation to create the control limits for the control chart. She also provides a Minitab project file. Both are available for download from the Curious Cat Management Improvement Library.

Related: Control Charts in Health CareCommon Cause VariationManaging with Control ChartsMeasurement and Data CollectionFourth Generation Management

Friday Fun: Correlation

Correlation doesn't imply causation

From the excellent xkcd comic.

Related: Correlation is Not CausationDoes the Data Deluge Make the Scientific Method Obsolete?Understanding DataTheory of KnowledgeWhat Makes Scientists Different :-) Dangers of Forgetting the Proxy Nature of DataSeeing Patterns Where None Exists

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

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