andrew_gelman_stats andrew_gelman_stats-2012 andrew_gelman_stats-2012-1201 knowledge-graph by maker-knowledge-mining
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Introduction: A recent article on global warming reminded me of the difficulty of letting the data speak. William Nordhaus shows the following graph: And then he writes: One of the reasons that drawing conclusions on temperature trends is tricky is that the historical temperature series is highly volatile, as can be seen in the figure. The presence of short-term volatility requires looking at long-term trends. A useful analogy is the stock market. Suppose an analyst says that because real stock prices have declined over the last decade (which is true), it follows that there is no upward trend. Here again, an examination of the long-term data would quickly show this to be incorrect. The last decade of temperature and stock market data is not representative of the longer-term trends. The finding that global temperatures are rising over the last century-plus is one of the most robust findings of climate science and statistics. I see what he’s saying, but first, I don’t find the st
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1 A recent article on global warming reminded me of the difficulty of letting the data speak. [sent-1, score-0.353]
2 William Nordhaus shows the following graph: And then he writes: One of the reasons that drawing conclusions on temperature trends is tricky is that the historical temperature series is highly volatile, as can be seen in the figure. [sent-2, score-0.794]
3 Suppose an analyst says that because real stock prices have declined over the last decade (which is true), it follows that there is no upward trend. [sent-5, score-0.425]
4 The last decade of temperature and stock market data is not representative of the longer-term trends. [sent-7, score-0.706]
5 The finding that global temperatures are rising over the last century-plus is one of the most robust findings of climate science and statistics. [sent-8, score-0.593]
6 Second, the debate over this particular claim is not about what was happening over the last century, it’s about what’s been happening over the past ten years or so. [sent-10, score-0.451]
7 The (uncomfortable, perhaps) take-home message from the above graph is that it is consistent with a continuing rise in temperature, and it’s also consistent with a leveling-off since the year 2000. [sent-11, score-0.81]
8 Nearly 25 years ago, Gary King and I came up with an improved estimate of the incumbency advantage in U. [sent-13, score-0.674]
9 Incumbency advantage was around 1 percentage point for the first half of the twentieth century, then it steadily rose, with no end in sight as of 1988. [sent-17, score-0.431]
10 Or maybe it rose until 1984 and then flattened out. [sent-19, score-0.268]
11 It’s the climate change story all over again, but this time with a lot less data and no physics to help us out. [sent-20, score-0.56]
12 Fortunately for our discussion, I returned to estimate the incumbency advantage a few years later, using a better statistical model and more data, going all the way to the year 2000. [sent-21, score-0.752]
13 ) Incumbency advantage doesn’t have anything to do with climate change—but the example illustrates the general difficulty of inferring trends from data alone. [sent-24, score-0.837]
14 To get back to the climate series: the data shown above are consistent with a continuing rise or a flattening of the curve. [sent-25, score-0.806]
15 “Over the past several years, the average global temperature during that time has in fact decreased. [sent-28, score-0.509]
16 Future trends are “virtually assuring us of about 30 years of global cooling. [sent-32, score-0.493]
17 The time trend is consistent with an increase, no trend, or even a future decrease. [sent-34, score-0.479]
18 Everybody knows this—scientists don’t study these climate time series in isolation—but then there can be a tendency to oversimplify as in Nordhaus’s discussion quoted above, which implies that the graph tells the story all by itself. [sent-36, score-0.757]
19 The graph is consistent with the story, which counts for something. [sent-37, score-0.337]
20 If you’re interested in the incumbency advantage in itself and not merely as an example of a difficult time series, see here for further discussion. [sent-40, score-0.573]
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Introduction: A recent article on global warming reminded me of the difficulty of letting the data speak. William Nordhaus shows the following graph: And then he writes: One of the reasons that drawing conclusions on temperature trends is tricky is that the historical temperature series is highly volatile, as can be seen in the figure. The presence of short-term volatility requires looking at long-term trends. A useful analogy is the stock market. Suppose an analyst says that because real stock prices have declined over the last decade (which is true), it follows that there is no upward trend. Here again, an examination of the long-term data would quickly show this to be incorrect. The last decade of temperature and stock market data is not representative of the longer-term trends. The finding that global temperatures are rising over the last century-plus is one of the most robust findings of climate science and statistics. I see what he’s saying, but first, I don’t find the st
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Introduction: I. State of the Climate report The National Oceanic and Atmospheric Administration recently released their “State of the Climate Report” for 2009 . The report has chapters discussing global climate (temperatures, water vapor, cloudiness, alpine glaciers,…); oceans (ocean heat content, sea level, sea surface temperatures, etc.); the arctic (sea ice extent, permafrost, vegetation, and so on); Antarctica (weather observations, sea ice extent,…), and regional climates. NOAA also provides a nice page that lets you display any of 11 relevant time-series datasets (land-surface air temperature, sea level, ocean heat content, September arctic sea-ice extent, sea-surface temperature, northern hemisphere snow cover, specific humidity, glacier mass balance, marine air temperature, tropospheric temperature, and stratospheric temperature). Each of the plots overlays data from several databases (not necessarily indepenedent of each other), and you can select which ones to include or leave
Introduction: My friend Seth, whom I know from Berkeley (we taught a course together on left-handedness), has a blog on topics ranging from thoughtful discussions of scientific evidence, to experiences with his unconventional weight-loss scheme, offbeat self-experimentation, and advocacy of fringe scientific theories, leavened with occasional dollops of cynicism and political extremism . I agree with Seth on some things but not others. ( Here’s Seth’s reason for not attempting a clinical trial of his diet.) Recently I was disturbed (but, I’m sorry to say, not surprised) to see Seth post the following: Predictions of climate models versus reality . I [Seth] have only seen careful prediction-vs-reality comparisons made by AGW [anthropogenic global warming] skeptics. Those who believe humans are dangerously warming the planet appear to be silent on this subject. In response, Phil commented : Funny, on the day you [Seth] made your post saying that you haven’t seen comparis
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Introduction: Jas sends along this paper (with Devin Caughey), entitled Regression-Discontinuity Designs and Popular Elections: Implications of Pro-Incumbent Bias in Close U.S. House Races, and writes: The paper shows that regression discontinuity does not work for US House elections. Close House elections are anything but random. It isn’t election recounts or something like that (we collect recount data to show that it isn’t). We have collected much new data to try to hunt down what is going on (e.g., campaign finance data, CQ pre-election forecasts, correct many errors in the Lee dataset). The substantive implications are interesting. We also have a section that compares in details Gelman and King versus the Lee estimand and estimator. I had a few comments: David Lee is not estimating the effect of incumbency; he’s estimating the effect of the incumbent party, which is a completely different thing. The regression discontinuity design is completely inappropriate for estimating the
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Introduction: After writing yesterday’s post , I was going through Solomon Hsiang’s blog and found a post pointing to three studies from researchers at business schools: Severe Weather and Automobile Assembly Productivity Gérard P. Cachon, Santiago Gallino and Marcelo Olivares Abstract: It is expected that climate change could lead to an increased frequency of severe weather. In turn, severe weather intuitively should hamper the productivity of work that occurs outside. But what is the effect of rain, snow, fog, heat and wind on work that occurs indoors, such as the production of automobiles? Using weekly production data from 64 automobile plants in the United States over a ten-year period, we find that adverse weather conditions lead to a significant reduction in production. For example, one additional day of high wind advisory by the National Weather Service (i.e., maximum winds generally in excess of 44 miles per hour) reduces production by 26%, which is comparable in order of magnitude t
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Introduction: A recent article on global warming reminded me of the difficulty of letting the data speak. William Nordhaus shows the following graph: And then he writes: One of the reasons that drawing conclusions on temperature trends is tricky is that the historical temperature series is highly volatile, as can be seen in the figure. The presence of short-term volatility requires looking at long-term trends. A useful analogy is the stock market. Suppose an analyst says that because real stock prices have declined over the last decade (which is true), it follows that there is no upward trend. Here again, an examination of the long-term data would quickly show this to be incorrect. The last decade of temperature and stock market data is not representative of the longer-term trends. The finding that global temperatures are rising over the last century-plus is one of the most robust findings of climate science and statistics. I see what he’s saying, but first, I don’t find the st
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Introduction: I. State of the Climate report The National Oceanic and Atmospheric Administration recently released their “State of the Climate Report” for 2009 . The report has chapters discussing global climate (temperatures, water vapor, cloudiness, alpine glaciers,…); oceans (ocean heat content, sea level, sea surface temperatures, etc.); the arctic (sea ice extent, permafrost, vegetation, and so on); Antarctica (weather observations, sea ice extent,…), and regional climates. NOAA also provides a nice page that lets you display any of 11 relevant time-series datasets (land-surface air temperature, sea level, ocean heat content, September arctic sea-ice extent, sea-surface temperature, northern hemisphere snow cover, specific humidity, glacier mass balance, marine air temperature, tropospheric temperature, and stratospheric temperature). Each of the plots overlays data from several databases (not necessarily indepenedent of each other), and you can select which ones to include or leave
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Introduction: After writing yesterday’s post , I was going through Solomon Hsiang’s blog and found a post pointing to three studies from researchers at business schools: Severe Weather and Automobile Assembly Productivity Gérard P. Cachon, Santiago Gallino and Marcelo Olivares Abstract: It is expected that climate change could lead to an increased frequency of severe weather. In turn, severe weather intuitively should hamper the productivity of work that occurs outside. But what is the effect of rain, snow, fog, heat and wind on work that occurs indoors, such as the production of automobiles? Using weekly production data from 64 automobile plants in the United States over a ten-year period, we find that adverse weather conditions lead to a significant reduction in production. For example, one additional day of high wind advisory by the National Weather Service (i.e., maximum winds generally in excess of 44 miles per hour) reduces production by 26%, which is comparable in order of magnitude t
Introduction: Solomon Hsiang shares some bad news: Persistently reduced labor productivity may be one of the largest economic impacts of anthropogenic climate change. . . . Two percent per degree Celsius . . . That’s the magic number for how worker productivity responds to warm/hot temperatures. In my 2010 PNAS paper , I [Hsiang] found that labor-intensive sectors of national economies decreased output by roughly 2.4% per degree C and argued that this looked suspiously like it came from reductions in worker output. Using a totally different method and dataset, Matt Neidell and Josh Graff Zivin found that labor supply in micro data fell by 1.8% per degree C. Both responses kicked in at around 26C. Chris Sheehan just sent me this NYT article on air conditioning , where they mention this neat natural experiment: [I]n the past year, [Japan] became an unwitting laboratory to study even more extreme air-conditioning abstinence, and the results have not been encouraging. After th
Introduction: Solomon Hsiang writes : I [Hsiang] have posted about high temperature inducing individuals to exhibit more violent behavior when driving, playing baseball and prowling bars. These cases are neat anecdotes that let us see the “pure aggression” response in lab-like conditions. But they don’t affect most of us too much. But violent crime in the real world affects everyone. Earlier, I posted a paper by Jacob et al. that looked at assault in the USA for about a decade – they found that higher temperatures lead to more assault and that the rise in violent crimes rose more quickly than the analogous rise in non-violent property-crime, an indicator that there is a “pure aggression” component to the rise in violent crime. A new working paper “Crime, Weather, and Climate Change” by recent Harvard grad Matthew Ranson puts together an impressive data set of all types of crime in USA counties for 50 years. The results tell the aggression story using street-level data very clearly [click to
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Introduction: The official announcement: The Excellence in Statistical Reporting Award for 2010 is presented to Felix Salmon for his body of work, which exemplifies the highest standards of scientific reporting. His insightful use of statistics as a tool to understanding the world of business and economics, areas that are critical in today’s economy, sets a new standard in statistical investigative reporting. Here are some examples: Tiger Woods Nigerian spammers How the government fudges job statistics This one is important to me. The idea is that “statistical reporting” is not just traditional science reporting (journalist talks with scientists and tries to understand the consensus) or science popularization or silly feature stories about the lottery. Salmon is doing investigative reporting using statistical thinking. Also, from a political angle, Salmon’s smart and quantitatively sophisticated work (as well as that of others such as Nate Silver) is an important counterweigh
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Introduction: According to the National Weather Service : What is a 100 year flood? A 100 year flood is an event that statistically has a 1% chance of occurring in any given year. A 500 year flood has a .2% chance of occurring and a 1000 year flood has a .1% chance of occurring. The accompanying map shows a part of Tennessee that in May 2010 had 1000-year levels of flooding. At first, it seems hard to believe that a 1000-year flood would have just happened to occur last year. But then, this is just a 1000-year flood for that particular place. I don’t really have a sense of the statistics of these events. How many 100-year, 500-year, and 1000-year flood events have been recorded by the Weather Service, and when have they occurred?
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Introduction: Don Coffin writes: A colleague of mine and I are doing a presentation for new faculty on a number of topics related to teaching. Our charge is to identify interesting issues and to find research-based information for them about how to approach things. So, what I wondered is, do you know of any published research dealing with the sort of issues about structuring a course and final exam in the ways you talk about in this blog post ? Some poking around in the usual places hasn’t turned anything up yet. I don’t really know the psychometrics literature but I imagine that some good stuff has been written on principles of test design. There are probably some good papers from back in the 1920s. Can anyone supply some references?
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