I ran across a one page handout in PDF format that discussed the difference between research and quality improvement. It was written from the perspective of the IRB (Institutional Review Board) at UMKC. It’s a nice summary, although the topic is a bit more complex than a single page handout might imply. This is a good starting point for deciding what type of study you want to do. Continue reading
Category Archives: Recommended
Recommended: R #6 in IEEE 2015 Top Programming Languages, Rising 3 Places
This Revolutions blog talks about a fairly rigorous evaluation of popular programming languages done by the Institute of Electrical and Electronics Engineers (known by most people by its acronym, IEEE). The list shows all programming languages, including general purpose programming languages. Java C, and C++ are at the top of the list, but R, a language pretty much dedicated to data analysis, is number 6 on the list (up three places from the previous year. Quite an impressive showing. I have mentioned another webpage, http://r4stats.com/articles/popularity/, that compares R and other statistical software packages, and that is worth reading as well. Continue reading
Recommended: PLOS ONE 2014 Reviewer Thank You
I don’t do nearly enough peer reviewing, in part because it is a thankless, anonymous task. But one journal editor sent me a nice email pointing out that my name was listed along with 80,000 other reviewer names for helping out with peer review of an article in 2014 for PLOS ONE. If you click on the link on the article and go down about 61,000 lines, you’ll find my name. Caution, the list is not quite perfectly in alphabetical order (Simons and Simonton should come AFTER Simon). Continue reading
Recommended: Beyond Bar and Line Graphs: Time for a New Data Presentation Paradigm
Many scientists rely on bar graphs and line graphs that effectively reduce your data to a single mean per group. Even with the addition of error bars, the whole process tends to hide important information. These authors suggest that scatterplots that show every data point would be a better way to present your research data. Continue reading
Recommended: An Introduction to Social Media for Scientists
It’s easy to mock social media, but these are important tools not just for sharing pictures of the food your eating but for informing your colleagues about your research. This article gives a nice overview of how to effectively use tools like Twitter, Facebook, Tumblr, and Pinterest. Continue reading
Recommended: Improving Bioscience Research Reporting: The ARRIVE Guidelines for Reporting Animal Research
A lot of people have adapted and updated the CONSORT Guidelines to reporting clinical trials to handle other types of research. One of these adaptations is the ARRIVE guidelines for reporting animal research. Many of these guidelines follow CONSORT quite closely, but there are details, such as documenting the species and strain of the experimental animals and describing the housing conditions, that are specific to animal experiments. Continue reading
Recommended: Rich Data, Poor Data
Nate Silver emphasizes an important point about when statistical models can really shine: when there is a rich source of data and lots of opportunities to test the predictive power of your models. This is why baseball statistics provide such a great platform for teaching modelling techniques. Continue reading
Recommended: Editorial (Basic and Applied Social Psychology)
Recommended does not always mean that I agree with what’s written. In this case, it means that this is something that is important to read because it offers an important perspective. And this editorial offers the perspective that all p-values and all confidence intervals are so fatally flawed that they are banned from all future publications in this journal. The editorial goes further to criticize most Bayesian methods because of the problems with the “Laplacian assumption.” The editorial authors have trouble with some of the ambiguities associated with creating a non-informative prior distribution that is, a prior distribution that represents a “state of ignorance.” They will accept Bayesian analyses on a case by case basis. Throwing out most Bayesian analyses, all p-values, and all confidence intervals makes you wonder what they will accept. They suggest larger than typical sample sizes, strong descriptive statistics (which they fail to define), and effect sizes. They believe that by “banning the NHSTP will have the effect of increasing the quality of submitted manuscripts by liberating authors from the stultified structure of NHSTP thinking thereby eliminating an important obstacle to creative thinking.” It’s worth debating this issue, though I think that these recommendations are far too extreme. Continue reading
Recommended: P-Values
Randall Munroe, author of the xkcd comic strip, will often comment on Statistics. This cartoon shows how p-values are typically interpreted. Continue reading
Recommended: New R Package: cdcfluview
I work a lot with secondary datasets and I’m always looking for new and interesting resources. There is a CDC site that tracks flu reports and with a bit of effort, you can get the raw data behind these reports. A blogger, hrbrmstr (Bob Rudis, if you dig long enough to find his real name), developed an R package that makes it easy to import this data into R. He illustrates the use of this package with a graph that shows some interesting trend lines across several major cities. Continue reading