The meme is talking about a common probability error that surveys have shown even doctors are prone to making.

Why you’re probably ok:

The rarity of the disease far exceeds the error rate of the positive test. Meaning, the disease occurs in 1 out of a million people, so if you are tested at random and show positive, you only have a 1 out of 30,000 chance (the 3% false-positive rate) of being the the 1 person who truly has the disease.

  • RamRabbit@lemmy.world
    31·
    3 months ago

    This is one of the main reasons doctors don’t ‘just give you a battery of tests’. Not only is that expensive, but if you are running dozens of tests, the chance one of them gives a false positive is pretty high. So now you not only wasted a pile of money, but you also think you have some rare disease you don’t actually have. So you waste even more time and money treating that disease you don’t have.

    Doctors run tests for things they think you might actually have, which diminishes the false positive chance.

    • themaninblack@lemmy.world
      2·
      3 months ago

      I have been thinking about this for a while.

      Would this actually diminish the false positive rate for the test? Would it just be more likely to get a true positive back?

      Or maybe would the false negative result be less likely?

      Does it depend on the sample group that was measured to get the accuracy statistics? If the sample group was random then does that actually make a difference?

      Doing my head in

  • over_clox@lemmy.world
    14·
    3 months ago

    I almost died of a dental abscess back in 2008, which led to a multi systemic failure. That was fun, but I’m still alive today.

    Fuckall with worrying about life anymore, if I ain’t dead yet, well I’m not dead. I’m doing okay BTW…

  • Bwaz@lemmy.worldEnglish
    2·
    3 months ago

    What statistician is this referring to? Certainly not one who understands probabilities. The first number has nothing to do with it. You tested positive, and there’s only a 3% chance that result is wrong. Time to settle your affairs.

    • drcobaltjedi@programming.dev
      10·
      3 months ago

      In a sample of 1 million people, 1 person will have the disease, 30,000 however will test positive for having the disease. Notice how the false positives count is way higher than the actual positive count.

      • Bwaz@lemmy.worldEnglish
        1·
        3 months ago

        How does that matter if I have a 97% chance of actually having the disease? A lot more people than I have won the lottery, doesn’t have a thing to do with whether I will.

        • drcobaltjedi@programming.dev
          2·
          3 months ago

          Its right 97% of the time. That does not mean you have a 97% chance of having the disease. The 3% error rate accounts for significantly more false positives than it accounts for false negatives on a disease that’s 1 in a million. Again, with a 3% error rate, there will be 30000 false positive test results in a million. 30000 in a million is a larger number than 1 in a million.

    • ilinamorato@lemmy.world
      1·
      3 months ago

      As far as I can see, you can’t really fear or rejoice with the results until you know the false positive/negative ratio.

  • somerandomperson1231@lemmy.dbzer0.comEnglish
    2·
    3 months ago

    Alright since I am actually currently learning about Bayes theorem. Assuming 97% accuracy means 3% chance of false negative and false positive. If you test positive. You have a 0.0032% of actually having the disease. If someone wants to double check me I encourage it.