I’m not a doctor. I’m not a researcher. I’m not a virologist. I don’t even claim to be an expert. I am, however, someone who likes to dig into data to try and parse out the truth. This is usually easier said than done.

As a computer scientist, I know that computer models can be made to show any outcome the requester wants. I also know that data and statistics are frequently twisted to show a particular outcome1. That is especially true when dealing with studies that are incomplete, lack rigorous testing, and are done by biased governments and researchers. Couple that with a hotly-debated and emotional issue like a pandemic and, well, you get A LOT of angry and fearful people.

For reference, until early-to-mid 2020, I was in the “pro-mask” camp. In fact, I was wearing a mask back when the CDC didn’t even recommend them! After all it seemed logical that, given the unknowns, I should be following the precautionary principle and doing what i can to protect myself and others. Especially if those steps are relative simple and effective. However, in really researching the issue for myself, I started to realize there are many holes in the mask2 wearing narrative.

Even now, however, I don’t consider myself anti-mask. I am, though, against being forced – under threat or penalty – against wearing it. There are way too many factors to broadly recommend wearing masks. For example, there are tons of studies showing zero evidence that mask protect from infectious contamination. Additionally, there are defined negatives and many, many unknowns about a public mask-wearing policy. In my opinion, this flips the precautionary principle around – it’s now smart to not broadly recommend wearing masks3. Either way, this site is my attempt to look at the science and cut through the noise.

  1. As Mark Twain said there are “There are three kinds of lies: lies, damned lies, and statistics” []
  2. figuratively and literally []
  3. if there’s enough convincing data, I reserve the right to change my opinion []