To Get Great (Statistical) Power, It Takes Great Responsibility
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Category Archives: Stats Corner
Forecasting the Fate of an Ecosystem: The Double-Edged Sword of Predictive Modelling
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Let’s get the humblebragging out of the way – this week a paper that I wrote was published in the Journal of Applied Ecology. It was a paper that I genuinely enjoyed writing, and it gives a tangible outcome – the forecasting of the establishment of invasive species within a region. The applications are obvious. Knowing where an invasive species is likely to pop up lets us detect it early and take action quickly.
Yet that very tangibility of the outcome has resulted in it being the paper of which I most fear the consequences. So in an exorcism of my general nerves (and as a soft disclaimer), I wanted to talk about why forecasting or predicting anything can be such a complicated undertaking for an ecologist.
Read moreHave I Seen You Before? Counting A Species With Mark-Recapture
When we hear news of a species resurgence or decline, it’s often accompanied by a number. Think “water vole populations have doubled in the last two decades” or “there are now only 1,400 komodo dragons left in the wild”. But how do scientists come up with those numbers? Surely they can’t have counted every single individual?
Read more“Wait, What Am I Even Saying?” Communicating Statistics To A Wide Audience
If we write about our statistical methods behind our ecology work, and none of our readers understand it, have we really communicated at all?
This month I’m getting meta. It’s been about a year and a half since I started writing the Stats Corner for this blog with the goal of demystifying some of the statistical methods that are used by ecologists every day. At the same time, I’ve been writing a book with Deborah Nolan called “Communicating with Data: The Art of Writing for Data Science.” The book was released this spring, so it seemed like a good time to reflect on writing about statistics accessibly.
Read moreThe How, Why, and When of Transforming Data
We’ve been out in the field, painstakingly collecting each butterfly and measuring its body length and wingspan. Now is the moment of truth. We’re about to make a plot and see if the assumptions we make about the relationship between the two measurements are backed up by a linear regression. Is the relationship between length and wingspan what we’d expect? Will a linear model be appropriate or are we going to have to break out the heavier machinery?
Read more“Those Things Are Evil”: Prediction Intervals in Mixed Models
Suppose we study salamanders and want to predict body mass based on their body length. We also want to account for different access to food and differing levels of competition at each site we’ve collected our salamanders from. So we fit a linear model with a random effect for site as we only have samples from a subset of sites. (Want a refresher on random effects? We’ve got you covered.)
Read moreFinding Balance on the Bias-Variance Seesaw
Building models is a tricky business. There are lots of decisions involved and competing motivations. Say we are an ecologist studying owl abundance in a park near our school. Our primary goal may be to have a good understanding of what is going on in our data. We don’t want to miss any important relationships between abundance and measurable factors about the landscape. Like if we didn’t include tree cover as an explanatory variable, we might have a model that is underfit since that variable would give us potential information about the availability of spots for owls to nest.
Read moreFixed, Mixed, and Random Effects: The Ecology Edition
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I’ve written about fixed, mixed, and random effects in linear models before (and others have too) but I think it’s time to approach the topic with some ecology motivation. What do these different types of effects mean to us in the wild and when might we need to use one over the other? Read on to learn more!
Read moreIt’s All Relative: Measuring Abundance In The Face of Detection Bias
There are many papers out there discussing estimates of abundance and occurrence of a variety of plants and animals. Sometimes you’ll also see references to relative abundance and relative occurrence. What makes researchers go for one estimate over the other? When might you face a similar choice? The goal of this post is to try to shed some light on when you might want to keep things relative.
Read moreThe Ecological Fallacy: What Does It Have To Do With Us?
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Ecology for the Masses 