Was PayPal trying to verify your identity or that you know your recipient's identity (who's coincidentally your mother)?
I'm surprised if PayPal expected you to know your recipient's birthday, but
"What's your mother's birthday?" would be a common question to verify your identity. They should have moved on to another question if you had a moral objection.
On the other hand, scammers will often ship goods to a nearby address and pick them up off the porch, so verifying that you know your recipient might actually be a fraud countermeasure.
One of my favorite data science factoids is how "regression" (to return to a former state) came to mean "prediction of a continuous variable".
In the late 1800s, Sir Francis Galton noticed that extremely tall or short parents usually had children that were not as tall or short as themselves, i.e. the children's heights were regressing (returning) to the mean. He collected hundreds of data points, graphed them, and estimated a coefficient describing this relationship, thereby inventing "linear regression."
We call them "regression" models simply because the first linear regression model was created to demonstrate the concept of regression to the mean.
They approximated income, education, and ethnicity at the neighborhood-level (400-600 people) based on census data. That doesn't seem granular enough to me if the poorest families in each neighborhood live closest to the major roads.
I’ve seen similar studies done in the U.K., where in cities plenty of relatively affluent people live directly on very congested, polluted roads, and the findings were identical. IIRC they found the worst effects were on people who had been living there for 40+ years - and speculated that that was down to lead exposure until TEL was banned - however the impacts of contemporary pollutants was still significant.
The problem is with multiple-day recovery periods after the surgery. The later in the week your surgery is scheduled, the more likely you'll overlap with the weekend when care is hypothesized to be lower quality.
I've had a bunch of surgeries and I wasn't given much of a choice as to when they were scheduled. These were orthopedic surgeries, but nothing urgent (stuff like a labrum repair or whatever). I was simply told to be at the hospital/surgery center at X time and that was that.
> Choosing Monday for a surgery increases your chance of success 2 times
The title seems poorly worded. The researchers studied mortality rate, not success of procedures. Since these were elective surgeries, I assume the success rate was high. If your surgery has a baseline success rate of 90%, what would it mean to increase that 2 times?
The conclusion of the study was instead that the odds of death were 44% higher on Friday compared to Monday, and 82% higher on the weekend compared to Monday. Basically, there was a gradual increase in the odds of death from Monday to Thursday, but a big jump up on Friday and again on the weekend.
You're looking to test the difference in proportions. I entered 0.93 / 1000 / 0.91 / 650 into this online calculator [1] and got a p-value of 0.14 which means that the difference is not statistically significant. Technically you would want to do some correction for multiple comparisons since your actual question is "are Americans more happy than workers in every other country?" not "are Americans more happy than Netherlanders specifically?" but it's a moot point since the difference already isn't significant.
A more important question is probably how the sampling was conducted. How did they guarantee a representative sample in each country? How did they account for the gig economy, small businesses, part-time employees, and employees paid under the table? So many ways a survey like this could be wrong.
When Irma was making landfall two years ago, I did an animation demonstrating this for r/DataIsBeautiful. [1] The real hurricane path is in red with the forecast at each timestamp in black. If you look at the forecasts through time, you can see that they're frequently off by a distance the width of Florida.
(This final graphic is an amalgamation of all the useful comments I got on the original thread. It was a really cool experience getting so much feedback so fast, like a giant collaboration with all of Reddit. You can see the original not-so-beautiful here. [2])
It would be great if this also had the cone overlaid on top of it. It is often interesting to read the forecaster's notes along with each of these graphics, as it is fairly common for the "line" to basically just be an average of several models without much expectation for it to be accurate in itself.
There are always potential issues when a machine learning algorithm is applied over time.
Example #1: Let's say that cancer rates are increasing over time and cameras are improving over time. You might end up with a weird artifact in your model that higher resolution images are more likely to indicate cancer.
Example #2: Let's say that cancer-detecting algorithms are widely successful and so someone makes an app that lets you upload images of skin and the app tells you the probability of you having cancer. Suddenly a model that was trained on suspicious lesions is being used on normal freckles that people uploaded for fun. You end up with a lot of false positives. Maybe you try to combat that by including images uploaded to the app (that you somehow obtain labels for). But now you have a model that predicts that photos taken in brightly lit medical offices are likely to be cancer and blurry images taken in bathroom mirrors are not cancer.
You could argue that Example #2 is more about the difference between training data and data to be scored, but the fact remains that outside of tightly controlled scenarios, the way data is collected nearly always changes in time and ends up affecting model performance in unexpected ways.
I'm surprised if PayPal expected you to know your recipient's birthday, but "What's your mother's birthday?" would be a common question to verify your identity. They should have moved on to another question if you had a moral objection.
On the other hand, scammers will often ship goods to a nearby address and pick them up off the porch, so verifying that you know your recipient might actually be a fraud countermeasure.