P.Mean >> The Monthly Mean >> Filler material for future newsletters (created 2010-02-04)
The following is filler material which I plan to use in future newsletters.
Feedback for November 2008 newsletter. I got some very positive feedback from three people last month. Two people cited the article on ANOVA versus regression as being the most helpful. The one suggestion for improvement was to drop the section on Wikipedia. I agree and in the next newsletter I might replace it with a "current events" entry that highlights a recent newspaper article touching on issues in Statistics. some suggested topics for future newsletters are principles for good graphical displays (such as the guidance from Edward Tufte) and important events in the history of Statistics.
Feedback for December 2008 newsletter. I only received feedback from two people (that's okay, since feedback is totally optional). One person had an interesting take on my newsletter. I don't think I read your newsletter to learn specific things as much as to be reminded of a way of thinking. I see so much use of statistics that I suspect is really lousy that it is useful to just immerse myself in the words of someone who knows what he's talking about, loves the field, and is able to teach/communicate well. I think I read your newsletter for inspiration ... for the fact that it returns me to a place of clarity and dare I say hope?? I'm not sure I always know what I'm talking about, but I do try to get people to think about how statistics are used in the real world. You don't have to be a rocket scientist or a brain surgeon to be able to critically evaluate the use of statistics in the real world. If one person finds this inspiring, then I'm happy. Another respondent liked my discussion on overfitting, but found my discussion on combining measures on different scales of measurement confusing. I'll see if I can clarify the latter point in a future webpage or newsletter entry. Both respondents had interesting suggestions for future discussion. One suggestion was about the use of propensity score, and whether it should used as a matching factor, a weighting factor, or a categorizing factor. This is something I am actively working on for a client, and I hope to put some of this up soon. Another suggestion was an explanation of adjusted odds ratios in logistic regression. I have some material on this at my old website and I'll try to elaborate further on this. A third suggestion was for "basic statistics" although there was some concern that this might be boring for other readers. I generally have found that people who are interested in help will invariably use the term "statistics for beginners." Actually, they will use a more perjorative term like "idiots" or "complete dummies" but this is not true. The people who ask for help are almost always very well educated and well read. They just lack experience in a particular area that I happen to know a few things about. In all the classes I've taught recently and all the webpages I've written, I've only received one request to make the discussion more technical. Even in people who I recognize understand statistics well, their desire is still for more basic information. I'm all in favor of getting a more solid understanding of the fundamentals. A final request came in more of the form of a complaint. In some studies, a single patient may have more than one procedure. This can lead to some confusing situations and the researchers themselves often fail to distinguish between patients and procedures. I think it is critical to always know how much data you have and of what type. The classic example that I encountered was a breastfeeding study with a data set of 84 infants born to 72 mothers because 12 of the mothers had given birth to twins. I'll try to write up something about this and about a closely related topic, pseudo-replication, for a future newsletter.
Feedback for January 2009 newsletter. January's newsletter produced three comments on the web survey and two emails. Someone was nice enough to point out a broken link in the January newsletter. When I link to my own website, iContact gets confused, so I need to manually insert the proper URLs for these links. I forgot one, which was a plug for my book. The correct link is http://www.pmean.com/Evidence.html. The same person also pointed out that when adults fall, it's not only the greater mass, but also the greater velocity due to the fact that we are up higher than kids. Greater mass and greater velocity is a double whammy. Cathy's pretty much recovered from the skating experience, by the way. I got a compliment for my section on crude versus adjusted comparisons. I was worried that it was a bit too technical. That person also wanted to see a discussion of generalized estimating equations in a future newsletter. I'm not sure I understand GEE models well enough to explain them clearly, but I will try to place them in the general context of repeated measures designs as a starting point. It may take a while for me to get something coherent written about this. This person also thought that "Nick News" was cute. Thanks! Two people emphasized a desire for basic statistics, how-to tutorials, and general concepts. That's my general goal. I'm not writing for people who already know Statistics inside and out. I also got a nice email from someone who liked my description of CART and the reference on overlapping confidence intervals. This person suggested some material on Bayesian models (more specifically, the Bayes factor). I had just come back from a conference on Bayesian Biostatistics, so the material is fresh in my mind. The hard part will be trying to make it accessible.
Feedback for Feburary 2009 newsletter. I got only one response to my feedback webpage. The respondent was relieved by my comments about small sample sizes, a reaction I had not expected. "The small samples problem isn't a big as i thought it was -- I worry about paediatric trials with 20-30 patients, but shouldn't so much." The entry was "Is there a sample size too small to allow efficient randomization?" and it is only one of several considerations. I elaborate on this in more detail in this month's newsletter. Still, if you're able to conduct a research study with 20-30 patients and get good precision on your confidence intervals, don't let anyone convince you that your sample size is too small. That person also asked for more material on generalized estimating equations (GEE), and other closely related topics. I've added a definition of GEE in this newsletter and hope to show some examples of various models in this area in future newsletters. I did get some nice compliments by email. Thanks! I'd brag about all the nice things you've said, but my email folders are in limbo thanks to a malfunctioning laptop.
Feedback for March/April 2009 newsletter. I got several nice comments about the March/April newsletter. There was appreciation for the explanation of GEE models, the rule of 15 for logistic regression, and the loss of power caused by dichotomization. Someone pointed out that the Java application on dichotomization is also available at www.bolderstats.com/jmsl/doc/medianSplit.html. I probably need to explain GEE models in more detail, though, and further clarify the Bayesian approach. There was a suggestion to explain what heterogeneity is. I presume this is heterogeneity in meta-analysis.
Feedback for May/June 2009 newsletter. Only two people sent feedback (that's okay, I know that you all love my newsletter). I got compliments on my description of mosaic plots. I also got compliments on the material on covariate adjustment from one person, but another person found that material confusing. Suggestions of future topics included meta-analysis statistics, Bayesian statistics, various methods for smoothing and checking for interactions with applications to survival analysis, and the problem with ROC curves. That's quite an ambitious list.
Feedback for July/August 2009 newsletter. Two people provided feedback to the last newsletter. I got positive comments about my article on weighted means and the JAMA article about changes to the primary outcome measure. Both were confused by the article on Bayesian data analysis. One had difficulty with interpreting conditional probability, especially with the order (A given B versus B given A). In spite of this, I was encouraged to write more about Bayesian statistics.
Feedback for September/October 2009 newsletter. Three people provided feedback to the last newsletter. I got praise for the article on rounding and re-ordering and the article on early stopping of a study and general praise for the newsletter. The only concern was about the basic formatting. I don't know what this means and I'd appreciate additional information from the person who left this comment or anyone else. Topics suggested for future newsletters include longitudinal analysis and robust analysis. I have been intending to write up a series of webpages about longitudinal analyses, but it's a complicated area and takes a lot of time to develop. I do hope to have something soon about this.
Feedback for November 2009 newsletter. Only two people provided feedback to the last newsletter. The Stephen Jay Gould article "The median is not the message" and the description of Kaplan-Meier curves drew praise. One person liked the simple explanation about the case mix index but the other person thought it was confusing. I got the excellent suggestion to talk about problems associated with missing data. It will take some effort, but I'll see what I can do.
Feedback for December 2009 newsletter. Two people provided feedback to the last newsletter. Both liked the description of the ROC curve. For one person, it was more a review and a clarification. I'm glad to do this. Sometimes the first time you learn something, it doesn't sink in. It's when you hear a second explanation, from a slightly different perspective, that it takes for an abstract idea to take hold. One person found the description about power and the minimum clinically relevant difference to be tough to follow. There were no suggestions this month for new topics. I do want to consider earlier suggestions about mixed models, generalized estimating equations, and multlilevel models in future newsletters, but these topics take a lot of time to prepare well. I'm also going to try to talk about missing data. It's a very important topic, but again one that takes some work to prepare well.
Feedback for January 2010 newsletter. Two people provided feedback to the last newsletter. One liked the description of the stem and leaf diagram, especially the explanation of how you might use two digits in the stems. The other liked the recent entry in the seven deadly sins of researchers (wrath) and the comments about heterogeneity in clinical trials. There was nothing unclear to either respondent. One suggestion for future topics was some of the issues associated with random effects meta-analysis, such as the use of confidence intervals versus prediction intervals. The other suggestion was how to do power calculations when you have the wrong standard deviation.
Feedback for February/March 2010 newsletter. Three people provided feedback to the last newsletter. Two liked my description of sensitivity and specificity and the other liked my explanation about weights. One person wanted more explanation of how to use control charts. I'm thinking of putting together a more elaborate set of webpages about quality improvement in general and that would include several different variations of the control chart. Suggestions for future newsletters include fixed versus random effects models and survival analysis. For the latter topic, I would note that my definition in the November 2009 newsletter was for the Kaplan-Meier plot. There is more to survival analysis than just Kaplan-Meier, of course, so I'll see what I can do. Let me apologize in advance though for this and earlier suggestions that I have not found the time to write just yet. These webpages take quite a bit of effort, especially the ones that describe some of the more advanced perspective.
Feedback for April 2010 newsletter. Only one person provided feedback to the last newsletter. That person liked the articles I had highlighted (Can we rely on the best trial? A comparison of individual trials and systematic reviews, and Convincing the Public to Accept New Medical Guidelines). That person also suggested that I review some more basic review topics like Type I and Type II errors. I do have definitions of these two terms at my old website but I still like the suggestions and will try to elaborate more on these definitions in a future newsletter.
Feedback for May/June 2010 newsletter. Three people provided feedback to the last newsletter. I got compliments on the article about why randomization doesn't always work, about Simpson's paradox (I think this person was thinking about my interaction article), and the link to the journal article about overdiagnosis of cancer. Several people were confused, though, about the description of interactions among two continuous variables in a linear regression model. I'll see if I can simplify it. There weren't a lot of suggestions about future topics and one person liked being surprised. A comment about the advantages of meta-analysis over simply counting the number of positive/negative studies was offered, though.
Feedback for July 2010 newsletter. Three people provided feedback to the last newsletter. I got compliments on the article about why randomization doesn't always work, about Simpson's paradox (I think this person was thinking about my interaction article), and the link to the journal article about overdiagnosis of cancer. Several people were confused, though, about the description of interactions among two continuous variables in a linear regression model. I'll see if I can simplify it. There weren't a lot of suggestions about future topics and one person liked being surprised. A comment about the advantages of meta-analysis over simply counting the number of positive/negative studies was offered, though.
Feedback for August 2010 newsletter. Three people provided feedback to the last newsletter. Two people liked the material about Structural Equations Modeling. One person challenged my approach to applying data from a t-test because I ignored the problems that non-normality could cause. There's not a clear consensus on how to best approach non-normality, but my major beef was with a different test, the test that checked the assumption of equality of variances. I'll try to explain in greater detail why I dislike this test in a later newsletter. One person suggested that I explain the special issues associated with equivalence and non-inferiority trials. This is an excellent suggestion. Another asked for examples of R scripts to solve certain problems. That might be a bit advanced for many of the readers of the newsletter, but I could always link to my website for people who would like to see this.
Feedback for the September/October 2010 newsletter. Two people provided feedback to the last newsletter. I got positive feedback on the link on poster preparation tips. There was some confusion about the section on unequal sample sizes and on the log transformation. Suggestions about future topics included selective outcome reporting and multivariate outcomes in systematic overviews.
Feedback for the November 2010 newsletter. One person provided feedback to the last newsletter. That person liked the description of an internal pilot study and the link to the Emily Rosa study (which was not on the newsletter, but could be found through my website). This person suggested an article about subgroup analysis, which I was able to accomodate in the current newsletter.
Feedback for December 2010 newsletter. One person provided feedback to the last newsletter. That person liked the description of an internal pilot study and the link to the Emily Rosa study. This person suggested an article about subgroup analysis, which I was able to accomodate in the current newsletter.
Feedback for January/February 2011 newsletter. Two people provided feedback to the last newsletter. I got compliments on my discussion of the Poisson distribution and the video on the relationship between wealth and health. I also got a compliment for stressing the need to worry about false positives when evaluating a p-value. The section on the Bonferroni adjustment and other adjustments for multiple comparisons caused more than a bit of confusion. I also got some helpful comments on how to improve the article on subgroup findings. I was encouraged to write more about subgroup analysis. I also got a request to write about "using stats for population management." I'm not quite sure what this last comment means.
Feedback for March/April 2011 newsletter.
Seven deadly sins summary:
Ideas for me to write about
Sample size for a propensity score adjusted model. Someone asked about how to calculate sample size in model where the outcome was adjusted using propensity scores. It's a tricky question, and your answer depends on exactly how you would use the propensity score.
One way to use the propensity score is to match observations with similar propensity score values. This gives you a matched pairs t-test rather than an independent samples t-test. So you might think, aha, I gain power because a matched pairs t-test is more powerful than an independent samples t-test. But not so fast. The process of matching will leave some of your observations unmatched. So there's a loss in power due to the unmatched observations. In fact, matching on the propensity score probably only makes sense when you have a large number of control patients compared to treatment patients, so it isn't hard to find a match for each treated patient. You don't lose so much in this setting because the unmatched control subjects were in the surplus anyway. In fact, leaving a bunch of the treated observations unmatched has the potential to bias your results.
So your power calculation has to account for the expected degree of correlation within a matched pair and the expected decline in sample size because of unmatched observations.
Another way to use the propensity score is to create strata based on cutpoints of the propensity score. Values within each strata have similar covariates, so should be more homogenous as a result. Again, you're thinking, aha, more power because of the stratification. But again it doesn't work that way. The samples sizes in the strata are likely to be unequal and seriously unequal if there is serious covariate imbalance. You'll have far too many treatment patients for strata representing a propensity score on one end of the scale and far too many control patients for strata representing a propensity score on the other end of the scale.
So in this setting your power calculation has to account for the expected amount of imbalance within each strata (which is related to the expected amount of covariate imbalance).
Finally, you can use the propensity score as a covariate in your model. One more time, you're thinking, aha, the covariate, if it is correlated with the outcome, will reduce variation and improve power. Yes, but there is multi-collinearity that you need to factor in. The propensity score is going to be correlated with your binary indicator of treatment versus control and it is going to be more strongly correlated if there is serious covariate imbalance.
So in this setting, you power calcuation has to account for the reduction in variation when the propensity score is added to the model, and it has to account for the collinearity induced by the propensity score.
The calculations described here are tedious, but not difficult. The hard part is figuring out reasonable estimates for loss of observation, imbalance within the strata, and collinearity. If you find this daunting, then just calculate power without accounting for the propensity score and then add a fudge factor that depends on how badly you expect the covariates to be imbalanced.
Propensity scores produce an unbiased comparison, but in most cases they do so at a cost in terms of lost power.
What is a descriptive study? (Distinguish between descriptive = no hypothesis versus descriptive = no experimental intervention0
What is the Hawthorne effect?
What is Simpson's paradox?
What is analysis of means (ANOM)?
Analytic versus enumerative studies.
Conflict of interest
I plan to be involved in a project where 100 cases are coded in terms of clincial and offence features. To enhance the design, I wanted to have a sample of cases rated by more than one coder. I was hoping for advice regarding either established guidlines (e.g 10-20% of cases) or literature on this subject. Most papers I have seen, where a sample is reviewed, the figures seem rather arbitrary.
To answer this question properly, you should be more specific about why you are collecting the data and what you plan to do with it. "Enhancing the design" is a bit vague.
In my experience, why most people include a sub-sample of observations with a second rater is to establish that the correlation between the two raters is sufficiently high to overcome any objections about the subjectivity of the coding. So select a sample size so that the 95% confidence interval is reasonably narrow. A correlation with a confidence interval from 0.1 to 0.8 does little to establish that your reliability is high. Try to get a sample size so that the lower limit of your confidence interval is above 0.3, 0.5, or 0.7, perhaps, assuming that the true correlation among raters is fairly strong (0.8, say).
A philosophical question about the extrapolation of a research finding
Someone posed a philosophical question on EDSTAT-L. Let's assume, he said, for the sake of argument that you've run a perfect randomized experiement at a single location. Let's assume, for sake of argument, that you have carefully defined the population of interest and have all of the "noise" factors. The experiment was done as well as could be done. If you find a statistically significant difference, to whom can you say your results apply to? There were several good responses. Here's what I wrote.
In theory no one, because the population of interest was sampled in the past, and even at the same site the present set of students is different than the previous set of students. Even if they are the exact same students, they are a tiny bit older and thus are different than they were when the experiment was done.
In practice, though, we extrapolate findings all the time. Our ability to extrapolate depends largely on a set of unprovable assumptions. It's impossible to get completely away from those assumptions, though repeated experimentation gives us some level of confidence about whether those assumptions are reasonable or unreasonable.
Clearly similarity of the two populations would be of help. They will never match perfectly, but a similar demographic mix, especially on those factors strongly associated with the outcome, is helpful. There are no hard and fast rules about this, of course. It just takes careful judgment.
If you want to see how well an experiment extrapolates to a different population, run that same experiment on the new population. That's been done many times and you do get a rough feel for when it is safe or unsafe to extrapolate. But you'll never know this with absolute certainty.
If you never tried to extrapolate without first having absolute certainty, then you would continually plunge your hand into a fire, because getting burned 100 times does not prove that you will get burned the 101st time. Maybe the first 100 times were just bad luck.
German tank problem: http://en.wikipedia.org/wiki/German_tank_problem#Exposition
Power for comparison of two Poisson means
What's a good value for Cronbach's alpha? http://www.childrensmercy.org/stats/weblog2004/CronbachAlpha.asp
I remain utterly convinced that posting images from publicly available documents, questioning their integrity when there is sufficient evidence to suggest a problem, is in no way grounds for a libel or defamation suit. In short—don't shoot the messenger. If you didn't want your scientific data to be questioned, you shouldn't have published it! —Paul Brookes, an associate professor at the University of Rochester Medical Center and a self-identified owner of the now-defunct Science Fraud blog, in a post explaining why libel threats had led him to shut down the site (as reported on Retraction Watch, Jan. 3, 2013) as quoted at http://www.the-scientist.com//?articles.view/articleNo/34187/title/Speaking-of-Science/
There's a cute quote by Stephen Senn in Statistical Issues in Drug Development (1997): Medical statistician: one who will not accept that Columbus discovered America ... because he said he was looking for India in the trial plan.
"It doesn't matter how beautiful your theory is, it doesn't matter how smart you are. If it doesn't agree with experiment, it's wrong" Richard Feynman, as quoted at http://thinkexist.com/quotation/it-doesn-t-matter-how-beautiful-your-theory-is-it/350072.html
"Statistics are no substitute for judgment." Clay, Henry Read more: http://quotationsbook.com/quote/37336/#ixzz1PsnVuHcj on Quotations Book
Counting is the religion of this generation it is its hope and its salvation. - Gertrude Stein, as quoted in The Emperor of All Maladies: A Biography of Cancer, Siddhartha Mukherjee.
Fiction is about the suspension of disbelief; science is about the suspension of belief. - James Porter
"In God we trust. All others must bring data."
David Cox: "There are no routine statistical questions, only questionable statistical routines"
"... no scientific worker has a fixed level of significance at which from year to year, and in all circumstances, he rejects hypotheses; he rather gives his mind to each particular case in the light of his evidence and his ideas." - Sir Ronald A. Fisher (1956)
"All scientific work is incomplete - whether it be observational or experimental. All scientific work is liable to be upset or modified by advancing knowledge. That does not confer upon us a freedom to ignore the knowledge we already have or postpone the action that it appears to demand at a given time." Sir Austin Bradford Hill, as quoted at Toxipedia http://toxipedia.org/display/toxipedia/Sir+Austin+Bradford+Hill
"On two occasions I have been asked [by members of Parliament!], 'Pray, Mr. Babbage, if you put into the machine wrong figures, will the right answers come out?' I am not able rightly to apprehend the kind of confusion of ideas that could provoke such a question." Charles Babbage, as quoted at http://stackoverflow.com/questions/17512/computer-language-puns-and-jokes
"If you're a politician, admitting you're wrong is a weakness, but if you're an engineer, you essentially want to be wrong half the time. If you do experiments and you're always right, then you aren't getting enough information out of those experiments. You want your experiment to be like the flip of a coin: You have no idea if it is going to come up heads or tails. You want to not know what the results are going to be." Peter Norvig, as quoted at http://www.slate.com/blogs/blogs/thewrongstuff/archive/2010/08/03/error-message-google-research-director-peter-norvig-on-being-wrong.aspx.
To call in the statistician after the experiment is done may be no more than asking him to perform a post-mortem examination: he may be able to say what the experiment died of. ~ Sir Ronald Aylmer Fisher
The plural of anecdote is not data. ~ Roger Brinner
The combination of some data and an aching desire for an answer does not ensure that a reasonable answer can be extracted from a given body of data. ~ John Tukey
In addition to what John (and others) have said, don't forget the good point raised by Bland & Altman (BMJ 2009;338:a3166). --- start excerpt --- The aversion to parametric methods for small samples may arise from the inability to assess the distribution shape when there are so few observations. How can we tell whether data follow a normal distribution if we have only a few observations? The answer is that we have not only the data to be analysed, but usually also experience of other sets of measurements of the same thing. In addition, general experience tells us that body size measurements are usually approximately normal, as are the logarithms of many blood concentrations and the square roots of counts. --- end excerpt ---
Cancer causes cell phones. http://xkcd.com/925/ or http://imgs.xkcd.com/comics/cell_phones.png
A statistical lament (after Joni Mitchell), Robert Dawson. http://www.lablit.com/article/646.
A frightening new study proves that new studies are frightening. Charles Fielding, as quoted on his Facebook page.
My bad joke about actuaries is that they're the ones who decided Statistics wasn't boring enough by itself, so they decided to mix it with insurance.
There's a story about two doctors who are floating above the countryside in a hot air balloon. They are drifting with the wind and enjoying the scenery, but after a couple of hours, they realize that they are totally lost. They see someone down on the ground, and shout down "Hello! Can you tell us where we are?" The person on the ground replies, "you're fifty feet up in the air, in a hot air balloon." One doctor turns to the other and says, "That person on the ground must be a statistician." "How did you know?" came astonished reply from the ground. "Only a statistician would provide an answer that was totally accurate and totally useless at the same time." In my stories, of course, the statistician always has the last word. "Very good. But I can also tell that you two are doctors." It was the doctors' turn to be astonished. The statistician explained. "Only a doctor would have such a good view of the area and still not have any idea they were."
Doctors love to use big words. These are the folks who take a simple ear ache and call it "otitis media." To them, a runny nose is "rhinorhea" and a tummy ache is "gastrointestinal distress." It's enough to make me produce lacrimal secretions.
R Through Excel. A Spreadsheet Interface for Statistics, Data Analysis, and Graphics. Heiberger, Richard M., Neuwirth, Erich.
Modeling Survival Data: Extending the Cox Model. Terry M. Therneau, Patricia M. Grambsch
The Truth Wears Off http://www.newyorker.com/reporting/2010/12/13/101213fa_fact_lehrer
Results Unproven, Robotic Surgery Wins Converts. http://www.nytimes.com/2010/02/14/health/14robot.html
In the City’s Vital Statistics, So Many Ways to Die by Accident http://www.nytimes.com/2010/02/16/nyregion/16accidents.html
Khamis, H. and Kepler, M.: Sample size in multiple regressions: 20 + 5k, Journal of Applied Statistical Science, to appear in Vol. 17. Also see http://ablejec.nib.si/as2007/appliedstatistics2007booklet.pdf (page 64).
It's not too recent, but an outstanding overview is: Biases in the interpretation and use of research results. RJ MacCoun. Annu Rev Psychol
1998: 49; 259-87.
Among the fascinating findings summarized in this paper:
"partisans on both sides of a dispute tend to see the exact same
media coverage as favoring their opponents� position"
"24 students favoring capital punishment and 24 opposing it were recruited; each group believed the existing evidence favored their views. They were then given descriptions of two fictitious studies, one supporting the deterrence hypothesis, the other failing to support it. For half the respondents, the prodeterrence paper used a cross-sectional methodology (cross-state homicide rates) and the antideterrent paper used a longitudinal methodology (within-state rates before and after capital punishment was adopted); for the remaining respondents, the methodologies were reversed. Each description contained a defense of the particular methodology and a critique of the opposing approach. Students received and provided initial reactions to each study's results before being given methodological details to evaluate. Analyses of student ratings of the quality and persuasiveness of these studies revealed a biased assimilation effect: students more favorably evaluated whichever study supported their initial views on the deterrent effect, irrespective of research methodology. Students' open-ended comments reveal how either methodology, cross-sectional or longitudinal, could be seen as superior or inferior, depending on how well its results accorded with one's initial views."
"people will feel less safe after a noncatastrophic technological breakdown if they already oppose the particular technology, but will feel more safe after such a breakdown if they support the technology."
I think that MacCoun also mentions somewhere about a study that showed people two contradictory articles on the same political topic. After reading both articles people became STRONGER in their beliefs. After all, they found a study that supported what they knew all along, and that other study must have been flawed somehow.
This paper should be required reading for anyone in Evidence-Based Medicine.
John P. A. Ioannidis. Why Most Published Research Findings Are False. PLoS Med. 2005;2(8):e124. Abstract: "There is increasing concern that most current published research findings are false. The probability that a research claim is true may depend on study power and bias, the number of other studies on the same question, and, importantly, the ratio of true to no relationships among the relationships probed in each scientific field. In this framework, a research finding is less likely to be true when the studies conducted in a field are smaller; when effect sizes are smaller; when there is a greater number and lesser preselection of tested relationships; where there is greater flexibility in designs, definitions, outcomes, and analytical modes; when there is greater financial and other interest and prejudice; and when more teams are involved in a scientific field in chase of statistical significance. Simulations show that for most study designs and settings, it is more likely for a research claim to be false than true. Moreover, for many current scientific fields, claimed research findings may often be simply accurate measures of the prevailing bias. In this essay, I discuss the implications of these problems for the conduct and interpretation of research." [Accessed January 6, 2009]. Available at: http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=1182327.
Susan A. Peters. Engaging with the Art and Science of Statistics. Mathematics Teacher. 2010;103(7):496. Abstract: "Statistics uses scientific tools but also requires the art of flexible and creative reasoning." [Accessed September 24, 2010]. Available at: http://www.nctm.org/eresources/view_media.asp?article_id=9145.
The Christmas carol, The Twelve Days of Christmas, is the musical equivalent of the 99 bottles of beer song. It involves a true love who is extraordinarily generous and gives one present on the first day of Chirstimas (a partridge in a pear tree), three presents on the second day of Christmas (two turtle doves and a partridge in a pear tree), six presents on the third day of Christmas (three French hens, two turtle doves, and a partridge in a pear tree). So after only three days, the true love has given 1+3+6 = 10 gifts. Two part question: 1. What is the total number of gifts given on the first through twelfth days of Christmas. 2. Give a formula for the number of gifts given by the nth day of Christmas.
John F. Hall -- http://surveyresearch.weebly.com
Jerry Dallal -- http://www.jerrydallal.com/
Edward Tufte, http://www.edwardtufte.com/tufte
Trend and variation. Animated short on statistics from Norwegian infotainment program Siffer. Produced by TeddyTV for NRK. Animation by Ole Christoffer Haga. http://www.youtube.com/watch?v=e0vj-0imOLw&feature=youtu.be
Statistics - Dream Job of the next decade. From a Keynote Presentation by Hal Varian - Chief Economist, Google, to the 2008 Almaden Institute - "Innovating with Information". The full presentation (as well as all the other presentations at the very interesting meeting) is available at http://www.almaden.ibm.com/institute/agenda.shtml Hal Varian makes the argument that with data in huge supply and statisticians in short supply, being a statistician has to be the 'really sexy job for the 2010s'. http://www.youtube.com/watch?v=D4FQsYTbLoI
Also see http://www.youtube.com/watch?v=tm3lZJdEvCc
Phil Murfet, Statistician: I Make Medicines. Phil Murfet explains how Pfizer is using the science of statistics to make better medicines for patients. http://www.youtube.com/watch?v=N89473ZLgOM
Important Massage from the Chief Statistician. Dr. Nussbaum has run the numbers, assessed the percentages, and evaluated the norms. He offers, with a small amount of confidence, 'whats in and whats out' for 2009. http://www.youtube.com/watch?v=cizJgk5SpmY
Statistician for The Unofficial Guide to Walt Disney World. Fred Hazelton, Statistician for the Unofficial Guide to Walt Disney World discusses his work with the guide and with touringplans.com on an Ottawa TV Morning Show. http://www.youtube.com/watch?v=8VWh7V-rwKM
Myth surrounding statisticians. Interview with Pravin Shekar, Chief-BizDev and New Initiatives, Dexterity, Chennai (www.dexterity.in), March 27, 2009, 1 pm. http://www.youtube.com/watch?v=oXfMPkMm1P8
Inspirational statistician. High school girl creates her own stat keeping software. http://www.youtube.com/watch?v=v9DpxkhphWM
World Statistics Day: Statistics All Around Us. US Census Bureau. Help the US Census Bureau celebrate World Statistics Day with the United Nations and statistical agencies across the globe. This video highlights some of the many benefits we receive from the statistical information provided by the U.S. federal statistical community. The key question: "What would our country -- our world -- be like, without statistics?" http://www.youtube.com/watch?v=piSCkkSvoMo
Why Statistics is Important: Saves lives, money and time. Doug Edwards. This story illustrates how important Statistics is to human life. It is presented in a true story format. I got the idea from reading the Book "Super Freakonomics" by S. Levitt and S. Dubner. The video illustrates the events that took place to solve the over 2000 early deaths of new mothers at the Vienna General Hospital between 1841 and 1846. http://www.youtube.com/watch?v=SBYBcENWZc4
Lies, Damned Lies, and Statistics (About TED Talks) "Excerpt: In a brilliantly tongue-in-cheek analysis, Sebastian Wernicke turns the tools of statistical analysis on TEDTalks, to come up with a metric for creating "the optimum TEDTalk" based on user ratings. How do you rate it?" Available at http://www.youtube.com/watch?v=1Totz8aa2Gg
Open-mindedness.; 2009. A look at some of the flawed thinking that prompts people who believe in certain non-scientific concepts to advise others who don't to be more open-minded. music © QualiaSoup . A note on spelling: A number of people have commented that 'close-minded' should be spelt 'closeD-minded'. This is incorrect. It's one of those cases in English where what you might suspect to be the case, isn't. The term is 'close-minded', in the same way that when one is reticent, one is said to be 'close-mouthed', not 'closeD-mouthed'. [Accessed August 24, 2010]. Available at: http://www.youtube.com/watch?v=T69TOuqaqXI&feature=youtube_gdata_player.
Dr. Andrew Newberg - Why God Doesn't Use Biostatistics.; 2008. An excerpt from Dr. Andrew Newberg's keynote lecture "Why God Doesn't Use Biostatistics: Science and the Study of the Mind, the Body, and Spirituality" presented on September 11, 2008 at the United Nations & Nour Foundation symposium, "Beyond the Mind-Body Problem: New Paradigms in the Science of Consciousness," inspired by the philosophy of Ostad Elahi. http://www.mindbodysymposium.com [Accessed August 25, 2010]. Available at: http://www.youtube.com/watch?v=V6iWazXDTps&feature=youtube_gdata_player.
http://www.youtube.com/watch?v=_sHa82UPPes&feature=email Rod Jackson is an epidemiologist in New Zealand and this video is a response from some of the medical students to his comments on risks of heart disease.
Gordon Guyatt. McMaster University > Evidence-Based Clinical Practice Workshop. Description: This website provides details on how to teach evidence-based clinical practice, through workshops offered at McMaster University, as well as through videos, such as "Making Sense of Likelihood Ratios" and "Understanding Odds Ratios." The site also offers some of the teaching handouts as PDF files. [Accessed January 24, 2010]. Available at: http://ebm.mcmaster.ca/.
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