How do I hate p-values? Let me count the ways…

[Note: a long post of interest only to people who care about data analysis and bad statistics, and maybe about the distant stars influencing your life.] By now, we should all be able to list the many reasons that p-values (or null-hypothesis-significance-testing, NHST) are awful: that “statistical significance” has nothing to do with effect size … Continue reading How do I hate p-values? Let me count the ways…

Learning about (machine) learning — Part I

Machine learning is everywhere these days, as we train computers to drive cars, play video games, and fold laundry. This intersects my lab’s research as well, which involves lots of computational image analysis (e.g.). Nearly everything my students and I write involves writing or applying particular algorithms to extract information from data. In the past … Continue reading Learning about (machine) learning — Part I

On the replication crisis in science and the twigs in my backyard

A long post, in which you’ll have to slog or scroll through several paragraphs to get to the real question: can we navigate using fallen sticks? These days we seem to be inundated with deeply flawed scientific papers, often featuring shaky conclusions boldly drawn from noisy data, results that can’t be replicated, or both. I … Continue reading On the replication crisis in science and the twigs in my backyard

I should think of a title involving the words “Small” and “Microscopy”

Our Physics Department Colloquium this week is on a topic I’m fond of: the analysis of super-resolution microscopy images. This occurrence isn’t surprising, since I invited the speaker, Alex Small, with whom I co-wrote a recent review paper on the subject. The problem that superresolution microscopy confronts is that it’s hard to see tiny things. … Continue reading I should think of a title involving the words “Small” and “Microscopy”

You can use any model you want, as long as it’s linear and has a positive slope

It is inherently challenging to use a model to make predictions beyond the range of data to which the model was fit — making predictions about the future, for example, based on the past and present. Or, as the Danish proverb more elegantly says, “It’s difficult to make predictions, especially about the future.” Still, there’s … Continue reading You can use any model you want, as long as it’s linear and has a positive slope