Several sources have pointed me to this neat web site of spurious correlations, showing graphically how, for example, the age of Miss America correlates with the number of murders by steam, hot vapours and hot objects, or my favorite: Though spurious correlations can be dangerous (and hilarious), it’s often useful to look for correlations in … Continue reading Reading this post? You get an “A”!
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”
I came across a short article at Science’s news site that notes that “Up to 1000 NIH Investigators Dropped Out Last Year” — i.e. the number of investigators funded by the NIH is presently dropping, a likely consequence of shrinking funding. The article includes this graph: What I find striking about the graph is the … Continue reading Culling the (science) herd?
This week’s bad graph* plots three-dimensional data with a squashed perspective, hiding any scatter of the points along the “out of plane” axis: The cognoscenti will recognize the plot as visualizing the outcome of principal component analysis (PCA), in this case applied to the microbial communities in mice fed dust from homes** with dogs (D) … Continue reading Bad Graph of the Week (in 3D!)
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