If you’re looking for a quantitatively oriented postdoc in ecology this position with Kiona Ogle is a great opportunity. I can’t vouch for her collaborator, but I’ve worked with Kiona and she is smart, has a good scientific philosophy, and is a patient & hard working collaborator – all goods signs for a postdoctoral mentor.
I had an interesting conversation with someone the other day that made me think I needed one last frequency distribution post in order to avoid causing some people to not move forward with addressing interesting questions. As a quantitative ecologist I spent a fair amount of time trying to figure out the best way to do things. In other words, I often want to know what the best method is available for answering a particular question.
Many of us have had the feeling that something is not right these days with the peer-review system in science. Whenever I chat with colleagues about the peer review system, two issues consistently crop up: an increasing number of review requests that we cannot possibly keep up with and/or reviews that seem to indicate a reviewer did not spend much time with the manuscript they were reviewing.
A couple of weeks ago we made it possible for folks to subscribe to JE using email. We did this because we realized that many scientists, even those who are otherwise computationally savvy, really haven’t embraced feed readers as a method of tracking information.
This is a table of contents of sorts for five posts on the visualization, fitting, and comparison of frequency distributions. The goal of these posts is to expose ecologists to the ideas and language related to good statistical practices for addressing frequency distribution data. The focus is on simple distributions and likelihood methods.
Summary Likelihood, likelihood, likelihood (and maybe some other complicated approaches), but definitely not r^2 values from fitting regressions to binned data. A bit more nitty gritty detail In addition to causing issues with parameter estimation, binning based methods are also inappropriate when trying to determine which distribution provides the best fit to empirical data.
Summary Don’t bin you’re data and fit a regression. Don’t use the CDF and fit a regression. Use maximum likelihood or other statistically grounded approaches that can typically be looked up on Wikipedia. A bit more detail OK, so you’ve visualized your data and after playing around a bit you have an idea of what the basic functional form of the model is. Now you want to estimate the parameters.
After writing about the importance of good RSS feeds for a particular subset of the academic community it occurred to me that part of the reason that we have such hit and miss implementations of feeds by journals is that most academics don’t even know what a feed is let alone actually use a feed reader. If this is you then we still want you to be able to get regular updates from JE, so last night I setup a new feed using Feedburner.
I’d recommend checking out this post by River Continua about an impressively sophisticated phishing scam targeted at academics. They’re going to catch a bunch of folks with this one. UPDATE: Apparently this is something that the EPA does that the EPA employee who wrote the original post was unaware of. They definitely need to rethink the composition of the email though as I would have been (and obviously was) equally suspicious.
