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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
    6. 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

      2 replies 0 retweets 1 like
    7. 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
    8. 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
    9. teamhendra‏ @teamhendra Aug 22
      Replying to @GidMK @K_Sheldrick and

      I was wondering you decided the entries in the dataset were recruited in the order presented in the Excel files?

      1 reply 0 retweets 0 likes
      Health Nerd‏Verified account @GidMK Aug 22
      Replying to @teamhendra @K_Sheldrick and

      No why?

      12:42 AM - 22 Aug 2021
      1 reply 0 retweets 0 likes
        1. New conversation
        2. teamhendra‏ @teamhendra Aug 22
          Replying to @GidMK @K_Sheldrick and

          Forget my question, I just re-read Kyle’s blog post.

          1 reply 0 retweets 2 likes
        3. teamhendra‏ @teamhendra Aug 22
          Replying to @teamhendra @GidMK @K_Sheldrick

          Looking at the Cadegiani spreadsheet, the first 192 participants appear to be all males in the 'non-hiperandrogenic' sub group - might this influence the unusual pairing pattern?

          1 reply 0 retweets 0 likes
        4. Show replies

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