Interpreting Data

During WW2, data was gathered related to airplanes coming back from combat missions. Among the things they recorded was the damage sustained by the airplanes. Bullet holes and parts ripped off of planes were noted down. This data was in turn fed back to the engineers who used it to make improvements.

At first glance, the data seemed to suggest that improvements should be made wherever damage was recorded. However, that would've been the wrong interpretation. The data received was from the survivors. All the damage recorded was non-fatal. It was the areas where the returning planes had no damage that needed to be improved because those were most likely to be the fatal regions. The engineers went ahead and improved those areas, and lives were saved.

On a similar note, I recently came across a study that ranked mortality rates relative to how many shots of vaccines people have taken. It showed that no shot at all is the most healthy situation. Unvaccinated people don't die in excess numbers relative to 2018 and 2019. However, the most unhealthy situation wasn't two shots and boosted, nor was it two shots an no booster. It was those with one shot only that were the most likely to meet God prematurely.

We might from this conclude that there's something unhealthy about taking one shot only. However, that's ignoring the fact that those who get an adverse effect from the vaccine often get this after the first shot. Most of the people who took only one shot were already sick. It's therefore no mystery that these people also die in greater numbers.

Those getting a booster are those who didn't get an adverse effect from the first two shots. These people have a high tolerance for the vaccine, and we get that the boosted people are the least likely to die early, second only to the unvaccinated. But this distribution changes over time.

The data shows single shot individuals to have high initial mortality rates. However, this fades. A year into the experiment, single shot mortality rates are on par with boosted individuals. The people who got ill have recovered or died, leaving a relatively healthy group of individuals for continued studies.

Spitfire

By Airwolfhound - commons.wikimedia, CC BY-SA 2.0, Link