June 2019


DoorLatch

Righty tighty, lefty loosy, when viewed using a mirror

by George Taniwaki

On a visit to the University of Washington Medical Center laboratory, I was asked to provide a urine specimen. As I approach the restroom, I notice the door is about 1m (40") wide to accommodate a wheelchair, which is good.

Both the outside and inside of the restroom door have solid metal lever handles. You push down to open the door. Levers are easier to grip than knobs and are now the preferred method to open and shut doors. Also good.

Once inside the restroom, there is an easy to grip lever above the handle that controls a lock for privacy. Good again.

As shown in the image above, the hinge for the lever is on top of a circular escutcheon and it flips left or right. But which way locks the door?

The rotation direction to lock the door is ambiguous. Apparently, there have been complaints, so someone printed a sign and taped it above the lever. But the sign is somewhat ambiguous as well since it is posted above the hinge, but the lever is below it.

Finally, it seems one of the lab techs used a grease pencil to indicate the direction to turn the lever to lock the door. But the grease is now smeared and illegible. The hand-drawn arrow points in the direction to lock the door, meaning the bottom toward the door edge or a counterclockwise turn. This is not standard in the U.S. and the likely source of confusion. Oh well, good thing I don’t have a shy bladder and don’t care if someone accidentally walks in on me.

Oddly, the convention to rotate a lock lever so that when the top points toward the door edge to mean the door is locked is not universal. In Japan, most locks are installed so that when the bottom points toward the door edge it is locked. You may notice this on some Japanese car doors.

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P.S. Ever have trouble knowing if a door opens toward you or away from you? To learn more about this design problem, read this blog post from 99% Invisible. Also watch the video. And read Don Norman’s book, The Design of Everyday Things.

[Update: Clarified the description of locks in Japan.]

GoogleRewardsReputableReporting

Google rewards reputable reporting, not left-wing politics, from The Economist

by George Taniwaki

A few months ago The Economist added a new feature to its back section called Graphic detail. It’s a pleasure to read because it nearly always contains bivariate plots where the x-axis is something more interesting than the date.

This week’s entry does not disappoint. It is entitled, Seek and you shall find and contains two charts (see above) with interesting x-axes. The charts analyze the impact of Google news search on the traffic a news source receives. It uses two independent measures, Accuracy score and Ideology score to rate different news sources. Accuracy and bias were determined using data from Adfontesmedia.com and Mediabiasfactcheck.com.

Many people claim that Google favors liberal news sources to the detriment of conservative views. Google claims it has a set of outside reviewers who check news sources for accuracy and reach. Point of view is not considered. However, one could imagine that a news source that has a strong point of view may report facts to match a point of view and that would reduce accuracy. As can be seen on the chart on the left, news sources with a strong ideological bias (darker red and blue dots) tend to have lower accuracy scores than less biased sources. I encourage you to go to the website because the data is interactive.

The dependent y-axis is the share of web traffic that comes from search engines. This is a bit problematic since if users believe that Google’s results are biased against their favorite news sources, they will visit it directly without using a search engine. Nonetheless, the data shows that search engine (mostly Google) share of web traffic increases with accuracy, not with ideology. That is, the plot on the left shows a linear relationship while the right plot does not.

Expected v. Actual

A separate experiment confirms the results. The Economist built a model to predict the number of news results appearing in 37 publications should receive from Google’s search engine based on their accuracy and their reach. It then compared the model results to actual search results on a “clean” computer using “a browser with no history, in a politically centrist part of Kansas.” (Why Kansas, you wonder? I’m guessing that is where the author lives.)

ExpectedVsActual

No bias detected, from The Economist

Again, no bias was detected. The difference between left and right are small and could be due to how they are defined, time of study, keywords searched, or other factors. The story is an excellent example of combining data from multiple sources, programming a bot to collect data, and visual display of statistical analysis.