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GidMK's profile
Health Nerd
Health Nerd
Health Nerd
Verified account
@GidMK

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Health NerdVerified account

@GidMK

Epidemiologist. Writer (Guardian, Observer etc). "Well known research trouble-maker". PhDing at @UoW Host of @senscipod Email gidmk.healthnerd@gmail.com he/him

Sydney, New South Wales
theguardian.com/profile/gideon…
Joined November 2015

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    1. J Chamie‏ @jjchamie Aug 20

      🇦🇺 COVID-19 in Australia Ivermectin interest keep growing as COVID seems unstoppable this time As a result a new hit job The evidence showing the effectiveness of ivermectin is so abundant, and the attacks so micro-elaborate that the authors only show their blatant intentions. https://twitter.com/GidMK/status/1428892398956908547 …pic.twitter.com/uFfVb9Fp7j

      14 replies 30 retweets 90 likes
    2. Health Nerd‏Verified account @GidMK Aug 21
      Replying to @jjchamie

      If you disagree with the numeric evaluation, I encourage you to download the dataset and prove myself and @K_Sheldrick wrong rather than this sort of bizarre rhetoric Neither of us is perfect, and it's entirely possible we're wrong

      12 replies 7 retweets 61 likes
    3. J Chamie‏ @jjchamie Aug 21
      Replying to @GidMK @K_Sheldrick

      I disagree. 1. You can't use Benford's law to analyse data restricted by a maximum or minimum number. 2. The trailing digits do follow a uniform distribution. 3. You owe an apology to @FlavioCadegiani and his team.

      3 replies 1 retweet 5 likes
    4. Kyle Sheldrick‏ @K_Sheldrick Aug 21
      Replying to @jjchamie @GidMK @FlavioCadegiani

      Sorry I can see we're both tagged, but was that directed to Gid or myself? I haven't seen anybody anywhere even propose a single possible non-fraud way the negative consecutive association could arise, including the authors, who I asked explicitly before posting.

      2 replies 0 retweets 5 likes
    5. Health Nerd‏Verified account @GidMK Aug 21
      Replying to @K_Sheldrick @jjchamie @FlavioCadegiani

      I also would point out that one or two distributions that violate the 2/3rd digits of Benfords is totally expected for a sample like this, what's weird is that every single variable has issues like this combined with the points you raised and the other errors with the data...

      1 reply 0 retweets 1 like
      Health Nerd‏Verified account @GidMK Aug 21
      Replying to @GidMK @K_Sheldrick and

      A useful visual example of age (which I hadn't looked at yet) - here's a graph from some real data I collected that beautifully follows Benford's vs the graph from the study (both 2nd digits)pic.twitter.com/UwQzbvO6Eb

      11:12 PM - 21 Aug 2021
      • 1 Like
      • ___Ornicar___
      2 replies 0 retweets 1 like
        1. New conversation
        2. Health Nerd‏Verified account @GidMK Aug 21
          Replying to @GidMK @K_Sheldrick and

          You can look at this in even more interesting ways. In the first 195 people, fully 22% have a BMI of 21.x, and 19% have a BMI of 22.x. Even with the slightly odd recruitment strategy, that's an amazingly narrow distribution of BMIs

          1 reply 0 retweets 1 like
        3. Health Nerd‏Verified account @GidMK Aug 21
          Replying to @GidMK @K_Sheldrick and

          Even more fascinatingly, there appear to be quite a few duplicated sets of weight/height. For example, there are 18 people in the first 195 with a weight of 58Kg. Of those, 7 have a height of exactly 1.63m 👀👀👀

          1 reply 0 retweets 0 likes
        4. Show replies
        1. New conversation
        2. J Chamie‏ @jjchamie Aug 21
          Replying to @GidMK @K_Sheldrick @FlavioCadegiani

          Again, you are using the wrong test. Weight or length are limited values and Benford is not applicable in these cases.

          1 reply 0 retweets 1 like
        3. Health Nerd‏Verified account @GidMK Aug 21
          Replying to @jjchamie @K_Sheldrick @FlavioCadegiani

          This is not Benford's 1st digit, which I agree would not apply, but the distribution of trailing digits which as you can see should indeed apply. There are noticeably fewer 0s than you would expect across every variable, for example

          1 reply 0 retweets 0 likes
        4. Show replies

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