Book Review: Standard Deviations, Flawed Assumptions, Tortured Data and Other Ways to Lie with Statistics
Years ago, when was asked to recommend investment books, I often suggested some about the psychological issues influencing investor behavior. They focused on investor fear and greed to show “what fools these mortals be.” Here are some examples:
Devil Take the Hindmost: A History of Financial Speculation
by Edward Chancellor, and
Extraordinary Popular Delusions and the Madness of Crowds
by Charles MacKay.
In recent years, there has been a wealth of material expanding on this theme in the form of behavioral finance and behavioral economics. I now suggest that investors do an internet search and read all they can on these topics. To better understand investing, you should know about herd mentality, recency bias, confirmation bias, overconfidence, overreaction, loss aversion, and the disposition effect.
An enjoyable introduction to this field is Richard Thaler’s Misbehaving: The Making of Behavioral Economics . Here is an extensive bibliography for those who want to do more in-depth study.
Importance of Statistical Analysis
Now that quantitative investment approaches (factors, indexing, rules-based models) are becoming prominent, you should also be able to intelligently evaluate quantitative methods. A lively book on the foundations of statistical analysis is The Seven Pillars of Statistical Wisdom by Stephen Stigler.
What I mostly recommend now to is Standard Deviations, Flawed Assumptions, Tortured Data and Other Ways to Lie with Statistics by economist Gary Smith. Everyone should be able to benefit from reading this book.
Smith’s premise is that we yearn to make an uncertain world more certain and to predict the unpredictable. This makes us susceptible to statistical deceptions. The investment world is especially susceptible now that it is more model-based and data driven.
It is easy to lie with statistics but hard to tell the truth without them. Smith takes up the challenge of sorting good from bad using insightful stories and entertaining examples. Here are some salient topics with real-world cases that Smith covers:
• Survivorship and self-selection biases
• Overemphasis on short-term results
• Underestimating the role of chance
• Results distorted by self-interest
• Correlation is not causation
• Regression to the mean
• Law of small numbers
• Confounding factors
• Misleading graphs
• Gamblers fallacy
Critical Thinking
Smith is not afraid to point out mistakes by the economic establishment. He mentions errors by University of Chicago economist Steven Levitt of Freakonomics fame. Smith also discusses research made popular by two Harvard professors, Reinhart and Rogoff. The professors concluded that a nation’s economic growth is imperiled when its ratio of government debt to GDP exceeds 90%. Smith points out serious problems with their work due to inadvertent errors, selective omissions of data, and questionable research procedures.
Publish or perish can contribute to errors in academic research. Economic self- interest, as in medical and financial research, can also cause errors. Smith helps us see how important it is to look at research critically instead of blindly accepting what is presented.
Theory Ahead of Data
Throughout his book Smith focuses on the potential perils associated with deriving theories from data. He gives examples of the Texas sharpshooter fallacy (aka the Feynman trap). Here a man with a gun but no skill fires a large number of bullets at the side of a barn. He then paints a bullseye around the spot with the most bullet holes. Another version is where the sharpshooter fires lots of bullets at lots of targets. He then finds a target he hits and forgets the rest. Predicting what the data looks like after examining the data is easy but meaningless. Smith says:
Data clusters are everywhere, even in random data. Someone who looks for an explanation will inevitably find one, but a theory that fits a data cluster is not persuasive evidence. The found explanation needs to make sense, and it needs to be tested with uncontaminated data.
Financial market researchers often use data to help invent a theory or develop a trading method. Theory or method generated by ransacking data is a perilous undertaking. Tortured data will always confess something. Pillaged data without theory leads to bogus inferences.
Data grubbing can uncover patterns that are nothing more than coincidence. Smith points to the South Seas stock bubble as an example where investors saw a pattern - buy the stock at a certain price and sell it at a higher price. But they didn’t think about whether t it made any sense.
Smith addresses those who take a quantitative approach to investing. He says quants have “a naïve confidence that historical patterns are a reliable guide to the future, and a dependence on theoretical assumptions that are mathematically convenient but dangerously unrealistic.”
Common Sense
Smith’s solution is to first make sure that one’s approach makes sense. He agrees with the great mathematician Pierre-Simon LaPlace who said probabilities are nothing but common sense reduced to calculation.
Smith says we should be cautious of calculating without thinking. I remember a case at the Harvard Business School where we set about looking at numbers trying to figure out why Smucker’s new ketchup was not doing well. The reason turned out to be that no one wanted to buy ketchup in a jam jar. We need to look past the numbers to see if what we are doing makes sense.
I often see data-derived trading approaches fit to past data without considering whether the approaches conform to known market principles. If one does come up with models having sensible explanations, they should then be tested on new data not corrupted by data grubbing. Whenever you deviate from the market portfolio, you are saying you are right and the market is wrong. There should be some really good reasons and plenty of supporting data before believing this to be true.
Self-Deception
I have seen some take the concept of dual momentum and make it more complicated with additional parameters, often with no logical basis. They may hold back half their data for model validation. They call this out-of-sample testing, but that is a questionable call.
Do you think you would hear about these models if their “out-of-sample” test showed poor results? Would they discard their models? Chances are they would search for other parameters that give satisfactory results on both the original and hold out data.
Momentum is robust enough that a test on hold out data may look okay from the beginning. But that is likely due to momentum’s overall strength and pervasiveness. It is doubtful you would have something better than what you would get with a simpler approach.
Keep It Simple
There are some areas I wish Smith had addressed more. First is the importance of having simple models, ala Occam’s razor. Simple models dramatically reduce the possibility of spurious results from overfitting data.
Overfitting is a serious problem in financial research. With enough parameters, you can fit almost any data to get attractive past results. But these often do not hold up going forward.
John von Neumann said that with four parameters he can fit an elephant, and with five he can make it wiggle its trunk.
In Data We Trust
The other area I wish Smith had addressed more is the importance of abundant data. This can help us avoid biased results.
According to de Moivre’s observation, accuracy is proportional to the square root of the number of observations. To have half the standard error, you need four times the data.
The stock market has major regime changes every 15 to 20 years. A model that worked well during one regime may fail during the next. We want a robust model that holds up well across different regimes. To determine that, you need plenty of data. In the words of Sherlock Holmes, “Data! Data! Data! I can’t make bricks without clay!”
Theories Without Data
Smith talks a lot about the issue of data groping without theory. But he also mentions the opposite problem of theory without adequate data analysis. Smith cites as an example the limit to growth theories of Malthus, Forrester, and Meadows. Smith contends they did not make any attempt to see whether historical data supported or refuted their theories. Most economists now dismiss these theories.
But Smith may not have considered later information on this topic. Here and here is information that makes this subject more provocative. Smith says it is good to think critically. This is true even when approaching a book as thoughtful as Smith’s.
In recent years, there has been a wealth of material expanding on this theme in the form of behavioral finance and behavioral economics. I now suggest that investors do an internet search and read all they can on these topics. To better understand investing, you should know about herd mentality, recency bias, confirmation bias, overconfidence, overreaction, loss aversion, and the disposition effect.
An enjoyable introduction to this field is Richard Thaler’s Misbehaving: The Making of Behavioral Economics . Here is an extensive bibliography for those who want to do more in-depth study.
Importance of Statistical Analysis
Now that quantitative investment approaches (factors, indexing, rules-based models) are becoming prominent, you should also be able to intelligently evaluate quantitative methods. A lively book on the foundations of statistical analysis is The Seven Pillars of Statistical Wisdom by Stephen Stigler.
What I mostly recommend now to is Standard Deviations, Flawed Assumptions, Tortured Data and Other Ways to Lie with Statistics by economist Gary Smith. Everyone should be able to benefit from reading this book.
Smith’s premise is that we yearn to make an uncertain world more certain and to predict the unpredictable. This makes us susceptible to statistical deceptions. The investment world is especially susceptible now that it is more model-based and data driven.
It is easy to lie with statistics but hard to tell the truth without them. Smith takes up the challenge of sorting good from bad using insightful stories and entertaining examples. Here are some salient topics with real-world cases that Smith covers:
• Survivorship and self-selection biases
• Overemphasis on short-term results
• Underestimating the role of chance
• Results distorted by self-interest
• Correlation is not causation
• Regression to the mean
• Law of small numbers
• Confounding factors
• Misleading graphs
• Gamblers fallacy
Critical Thinking
Smith is not afraid to point out mistakes by the economic establishment. He mentions errors by University of Chicago economist Steven Levitt of Freakonomics fame. Smith also discusses research made popular by two Harvard professors, Reinhart and Rogoff. The professors concluded that a nation’s economic growth is imperiled when its ratio of government debt to GDP exceeds 90%. Smith points out serious problems with their work due to inadvertent errors, selective omissions of data, and questionable research procedures.
Publish or perish can contribute to errors in academic research. Economic self- interest, as in medical and financial research, can also cause errors. Smith helps us see how important it is to look at research critically instead of blindly accepting what is presented.
Theory Ahead of Data
Throughout his book Smith focuses on the potential perils associated with deriving theories from data. He gives examples of the Texas sharpshooter fallacy (aka the Feynman trap). Here a man with a gun but no skill fires a large number of bullets at the side of a barn. He then paints a bullseye around the spot with the most bullet holes. Another version is where the sharpshooter fires lots of bullets at lots of targets. He then finds a target he hits and forgets the rest. Predicting what the data looks like after examining the data is easy but meaningless. Smith says:
Data clusters are everywhere, even in random data. Someone who looks for an explanation will inevitably find one, but a theory that fits a data cluster is not persuasive evidence. The found explanation needs to make sense, and it needs to be tested with uncontaminated data.
Financial market researchers often use data to help invent a theory or develop a trading method. Theory or method generated by ransacking data is a perilous undertaking. Tortured data will always confess something. Pillaged data without theory leads to bogus inferences.
Data grubbing can uncover patterns that are nothing more than coincidence. Smith points to the South Seas stock bubble as an example where investors saw a pattern - buy the stock at a certain price and sell it at a higher price. But they didn’t think about whether t it made any sense.
Smith addresses those who take a quantitative approach to investing. He says quants have “a naïve confidence that historical patterns are a reliable guide to the future, and a dependence on theoretical assumptions that are mathematically convenient but dangerously unrealistic.”
Common Sense
Smith’s solution is to first make sure that one’s approach makes sense. He agrees with the great mathematician Pierre-Simon LaPlace who said probabilities are nothing but common sense reduced to calculation.
Smith says we should be cautious of calculating without thinking. I remember a case at the Harvard Business School where we set about looking at numbers trying to figure out why Smucker’s new ketchup was not doing well. The reason turned out to be that no one wanted to buy ketchup in a jam jar. We need to look past the numbers to see if what we are doing makes sense.
I often see data-derived trading approaches fit to past data without considering whether the approaches conform to known market principles. If one does come up with models having sensible explanations, they should then be tested on new data not corrupted by data grubbing. Whenever you deviate from the market portfolio, you are saying you are right and the market is wrong. There should be some really good reasons and plenty of supporting data before believing this to be true.
Self-Deception
I have seen some take the concept of dual momentum and make it more complicated with additional parameters, often with no logical basis. They may hold back half their data for model validation. They call this out-of-sample testing, but that is a questionable call.
Do you think you would hear about these models if their “out-of-sample” test showed poor results? Would they discard their models? Chances are they would search for other parameters that give satisfactory results on both the original and hold out data.
Momentum is robust enough that a test on hold out data may look okay from the beginning. But that is likely due to momentum’s overall strength and pervasiveness. It is doubtful you would have something better than what you would get with a simpler approach.
Keep It Simple
There are some areas I wish Smith had addressed more. First is the importance of having simple models, ala Occam’s razor. Simple models dramatically reduce the possibility of spurious results from overfitting data.
Overfitting is a serious problem in financial research. With enough parameters, you can fit almost any data to get attractive past results. But these often do not hold up going forward.
John von Neumann said that with four parameters he can fit an elephant, and with five he can make it wiggle its trunk.
The other area I wish Smith had addressed more is the importance of abundant data. This can help us avoid biased results.
According to de Moivre’s observation, accuracy is proportional to the square root of the number of observations. To have half the standard error, you need four times the data.
The stock market has major regime changes every 15 to 20 years. A model that worked well during one regime may fail during the next. We want a robust model that holds up well across different regimes. To determine that, you need plenty of data. In the words of Sherlock Holmes, “Data! Data! Data! I can’t make bricks without clay!”
Theories Without Data
Smith talks a lot about the issue of data groping without theory. But he also mentions the opposite problem of theory without adequate data analysis. Smith cites as an example the limit to growth theories of Malthus, Forrester, and Meadows. Smith contends they did not make any attempt to see whether historical data supported or refuted their theories. Most economists now dismiss these theories.
But Smith may not have considered later information on this topic. Here and here is information that makes this subject more provocative. Smith says it is good to think critically. This is true even when approaching a book as thoughtful as Smith’s.
Years ago, when was asked to recommend investment books, I often suggested some about the psychological issues influencing investor behavior. They focused on investor fear and greed to show “what fools these mortals be.” Here are some examples:
Devil Take the Hindmost: A History of Financial Speculation
by Edward Chancellor, and
Extraordinary Popular Delusions and the Madness of Crowds
by Charles MacKay.
In recent years, there has been a wealth of material expanding on this theme in the form of behavioral finance and behavioral economics. I now suggest that investors do an internet search and read all they can on these topics. To better understand investing, you should know about herd mentality, recency bias, confirmation bias, overconfidence, overreaction, loss aversion, and the disposition effect.
An enjoyable introduction to this field is Richard Thaler’s Misbehaving: The Making of Behavioral Economics . Here is an extensive bibliography for those who want to do more in-depth study.
Importance of Statistical Analysis
Now that quantitative investment approaches (factors, indexing, rules-based models) are becoming prominent, you should also be able to intelligently evaluate quantitative methods. A lively book on the foundations of statistical analysis is The Seven Pillars of Statistical Wisdom by Stephen Stigler.
What I mostly recommend now to is Standard Deviations, Flawed Assumptions, Tortured Data and Other Ways to Lie with Statistics by economist Gary Smith. Everyone should be able to benefit from reading this book.
Smith’s premise is that we yearn to make an uncertain world more certain and to predict the unpredictable. This makes us susceptible to statistical deceptions. The investment world is especially susceptible now that it is more model-based and data driven.
It is easy to lie with statistics but hard to tell the truth without them. Smith takes up the challenge of sorting good from bad using insightful stories and entertaining examples. Here are some salient topics with real-world cases that Smith covers:
• Survivorship and self-selection biases
• Overemphasis on short-term results
• Underestimating the role of chance
• Results distorted by self-interest
• Correlation is not causation
• Regression to the mean
• Law of small numbers
• Confounding factors
• Misleading graphs
• Gamblers fallacy
Critical Thinking
Smith is not afraid to point out mistakes by the economic establishment. He mentions errors by University of Chicago economist Steven Levitt of Freakonomics fame. Smith also discusses research made popular by two Harvard professors, Reinhart and Rogoff. The professors concluded that a nation’s economic growth is imperiled when its ratio of government debt to GDP exceeds 90%. Smith points out serious problems with their work due to inadvertent errors, selective omissions of data, and questionable research procedures.
Publish or perish can contribute to errors in academic research. Economic self- interest, as in medical and financial research, can also cause errors. Smith helps us see how important it is to look at research critically instead of blindly accepting what is presented.
Theory Ahead of Data
Throughout his book Smith focuses on the potential perils associated with deriving theories from data. He gives examples of the Texas sharpshooter fallacy (aka the Feynman trap). Here a man with a gun but no skill fires a large number of bullets at the side of a barn. He then paints a bullseye around the spot with the most bullet holes. Another version is where the sharpshooter fires lots of bullets at lots of targets. He then finds a target he hits and forgets the rest. Predicting what the data looks like after examining the data is easy but meaningless. Smith says:
Data clusters are everywhere, even in random data. Someone who looks for an explanation will inevitably find one, but a theory that fits a data cluster is not persuasive evidence. The found explanation needs to make sense, and it needs to be tested with uncontaminated data.
Financial market researchers often use data to help invent a theory or develop a trading method. Theory or method generated by ransacking data is a perilous undertaking. Tortured data will always confess something. Pillaged data without theory leads to bogus inferences.
Data grubbing can uncover patterns that are nothing more than coincidence. Smith points to the South Seas stock bubble as an example where investors saw a pattern - buy the stock at a certain price and sell it at a higher price. But they didn’t think about whether t it made any sense.
Smith addresses those who take a quantitative approach to investing. He says quants have “a naïve confidence that historical patterns are a reliable guide to the future, and a dependence on theoretical assumptions that are mathematically convenient but dangerously unrealistic.”
Common Sense
Smith’s solution is to first make sure that one’s approach makes sense. He agrees with the great mathematician Pierre-Simon LaPlace who said probabilities are nothing but common sense reduced to calculation.
Smith says we should be cautious of calculating without thinking. I remember a case at the Harvard Business School where we set about looking at numbers trying to figure out why Smucker’s new ketchup was not doing well. The reason turned out to be that no one wanted to buy ketchup in a jam jar. We need to look past the numbers to see if what we are doing makes sense.
I often see data-derived trading approaches fit to past data without considering whether the approaches conform to known market principles. If one does come up with models having sensible explanations, they should then be tested on new data not corrupted by data grubbing. Whenever you deviate from the market portfolio, you are saying you are right and the market is wrong. There should be some really good reasons and plenty of supporting data before believing this to be true.
Self-Deception
I have seen some take the concept of dual momentum and make it more complicated with additional parameters, often with no logical basis. They may hold back half their data for model validation. They call this out-of-sample testing, but that is a questionable call.
Do you think you would hear about these models if their “out-of-sample” test showed poor results? Would they discard their models? Chances are they would search for other parameters that give satisfactory results on both the original and hold out data.
Momentum is robust enough that a test on hold out data may look okay from the beginning. But that is likely due to momentum’s overall strength and pervasiveness. It is doubtful you would have something better than what you would get with a simpler approach.
Keep It Simple
There are some areas I wish Smith had addressed more. First is the importance of having simple models, ala Occam’s razor. Simple models dramatically reduce the possibility of spurious results from overfitting data.
Overfitting is a serious problem in financial research. With enough parameters, you can fit almost any data to get attractive past results. But these often do not hold up going forward.
John von Neumann said that with four parameters he can fit an elephant, and with five he can make it wiggle its trunk.
In Data We Trust
The other area I wish Smith had addressed more is the importance of abundant data. This can help us avoid biased results.
According to de Moivre’s observation, accuracy is proportional to the square root of the number of observations. To have half the standard error, you need four times the data.
The stock market has major regime changes every 15 to 20 years. A model that worked well during one regime may fail during the next. We want a robust model that holds up well across different regimes. To determine that, you need plenty of data. In the words of Sherlock Holmes, “Data! Data! Data! I can’t make bricks without clay!”
Theories Without Data
Smith talks a lot about the issue of data groping without theory. But he also mentions the opposite problem of theory without adequate data analysis. Smith cites as an example the limit to growth theories of Malthus, Forrester, and Meadows. Smith contends they did not make any attempt to see whether historical data supported or refuted their theories. Most economists now dismiss these theories.
But Smith may not have considered later information on this topic. Here and here is information that makes this subject more provocative. Smith says it is good to think critically. This is true even when approaching a book as thoughtful as Smith’s.
In recent years, there has been a wealth of material expanding on this theme in the form of behavioral finance and behavioral economics. I now suggest that investors do an internet search and read all they can on these topics. To better understand investing, you should know about herd mentality, recency bias, confirmation bias, overconfidence, overreaction, loss aversion, and the disposition effect.
An enjoyable introduction to this field is Richard Thaler’s Misbehaving: The Making of Behavioral Economics . Here is an extensive bibliography for those who want to do more in-depth study.
Importance of Statistical Analysis
Now that quantitative investment approaches (factors, indexing, rules-based models) are becoming prominent, you should also be able to intelligently evaluate quantitative methods. A lively book on the foundations of statistical analysis is The Seven Pillars of Statistical Wisdom by Stephen Stigler.
What I mostly recommend now to is Standard Deviations, Flawed Assumptions, Tortured Data and Other Ways to Lie with Statistics by economist Gary Smith. Everyone should be able to benefit from reading this book.
Smith’s premise is that we yearn to make an uncertain world more certain and to predict the unpredictable. This makes us susceptible to statistical deceptions. The investment world is especially susceptible now that it is more model-based and data driven.
It is easy to lie with statistics but hard to tell the truth without them. Smith takes up the challenge of sorting good from bad using insightful stories and entertaining examples. Here are some salient topics with real-world cases that Smith covers:
• Survivorship and self-selection biases
• Overemphasis on short-term results
• Underestimating the role of chance
• Results distorted by self-interest
• Correlation is not causation
• Regression to the mean
• Law of small numbers
• Confounding factors
• Misleading graphs
• Gamblers fallacy
Critical Thinking
Smith is not afraid to point out mistakes by the economic establishment. He mentions errors by University of Chicago economist Steven Levitt of Freakonomics fame. Smith also discusses research made popular by two Harvard professors, Reinhart and Rogoff. The professors concluded that a nation’s economic growth is imperiled when its ratio of government debt to GDP exceeds 90%. Smith points out serious problems with their work due to inadvertent errors, selective omissions of data, and questionable research procedures.
Publish or perish can contribute to errors in academic research. Economic self- interest, as in medical and financial research, can also cause errors. Smith helps us see how important it is to look at research critically instead of blindly accepting what is presented.
Theory Ahead of Data
Throughout his book Smith focuses on the potential perils associated with deriving theories from data. He gives examples of the Texas sharpshooter fallacy (aka the Feynman trap). Here a man with a gun but no skill fires a large number of bullets at the side of a barn. He then paints a bullseye around the spot with the most bullet holes. Another version is where the sharpshooter fires lots of bullets at lots of targets. He then finds a target he hits and forgets the rest. Predicting what the data looks like after examining the data is easy but meaningless. Smith says:
Data clusters are everywhere, even in random data. Someone who looks for an explanation will inevitably find one, but a theory that fits a data cluster is not persuasive evidence. The found explanation needs to make sense, and it needs to be tested with uncontaminated data.
Financial market researchers often use data to help invent a theory or develop a trading method. Theory or method generated by ransacking data is a perilous undertaking. Tortured data will always confess something. Pillaged data without theory leads to bogus inferences.
Data grubbing can uncover patterns that are nothing more than coincidence. Smith points to the South Seas stock bubble as an example where investors saw a pattern - buy the stock at a certain price and sell it at a higher price. But they didn’t think about whether t it made any sense.
Smith addresses those who take a quantitative approach to investing. He says quants have “a naïve confidence that historical patterns are a reliable guide to the future, and a dependence on theoretical assumptions that are mathematically convenient but dangerously unrealistic.”
Common Sense
Smith’s solution is to first make sure that one’s approach makes sense. He agrees with the great mathematician Pierre-Simon LaPlace who said probabilities are nothing but common sense reduced to calculation.
Smith says we should be cautious of calculating without thinking. I remember a case at the Harvard Business School where we set about looking at numbers trying to figure out why Smucker’s new ketchup was not doing well. The reason turned out to be that no one wanted to buy ketchup in a jam jar. We need to look past the numbers to see if what we are doing makes sense.
I often see data-derived trading approaches fit to past data without considering whether the approaches conform to known market principles. If one does come up with models having sensible explanations, they should then be tested on new data not corrupted by data grubbing. Whenever you deviate from the market portfolio, you are saying you are right and the market is wrong. There should be some really good reasons and plenty of supporting data before believing this to be true.
Self-Deception
I have seen some take the concept of dual momentum and make it more complicated with additional parameters, often with no logical basis. They may hold back half their data for model validation. They call this out-of-sample testing, but that is a questionable call.
Do you think you would hear about these models if their “out-of-sample” test showed poor results? Would they discard their models? Chances are they would search for other parameters that give satisfactory results on both the original and hold out data.
Momentum is robust enough that a test on hold out data may look okay from the beginning. But that is likely due to momentum’s overall strength and pervasiveness. It is doubtful you would have something better than what you would get with a simpler approach.
Keep It Simple
There are some areas I wish Smith had addressed more. First is the importance of having simple models, ala Occam’s razor. Simple models dramatically reduce the possibility of spurious results from overfitting data.
Overfitting is a serious problem in financial research. With enough parameters, you can fit almost any data to get attractive past results. But these often do not hold up going forward.
John von Neumann said that with four parameters he can fit an elephant, and with five he can make it wiggle its trunk.
The other area I wish Smith had addressed more is the importance of abundant data. This can help us avoid biased results.
According to de Moivre’s observation, accuracy is proportional to the square root of the number of observations. To have half the standard error, you need four times the data.
The stock market has major regime changes every 15 to 20 years. A model that worked well during one regime may fail during the next. We want a robust model that holds up well across different regimes. To determine that, you need plenty of data. In the words of Sherlock Holmes, “Data! Data! Data! I can’t make bricks without clay!”
Theories Without Data
Smith talks a lot about the issue of data groping without theory. But he also mentions the opposite problem of theory without adequate data analysis. Smith cites as an example the limit to growth theories of Malthus, Forrester, and Meadows. Smith contends they did not make any attempt to see whether historical data supported or refuted their theories. Most economists now dismiss these theories.
But Smith may not have considered later information on this topic. Here and here is information that makes this subject more provocative. Smith says it is good to think critically. This is true even when approaching a book as thoughtful as Smith’s.
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