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GidMK's profile
Health Nerd
Health Nerd
Health Nerd
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@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. Health Nerd‏Verified account @GidMK 8 Sep 2018

      Health Nerd Retweeted David Nunan

      The fact is that food-frequency questionnaires are flawed BUT The flaws are EQUALLY DISTRIBUTED This means that everyone answers them wrong, but in the same ways (usually underestimating calories) which makes them extremely useful for researchhttps://twitter.com/dnunan79/status/1038326067298217985 …

      Health Nerd added,

      David Nunan @dnunan79
      TWITTER convo: LCHF advocate: “data from FFQs are garbage. I therefore ignore nutritional epi studies using them. Scientist who published something they like: “Someone should do a meta-analysis of all the nutri epi studies (that use FFQ) LCHF advocates: “Yes” ❤️ + RT Lolz
      Show this thread
      1 reply 3 retweets 5 likes
      Show this thread
      Health Nerd‏Verified account @GidMK 8 Sep 2018

      In much the same way, men almost always overestimate their height and everyone thinks they exercise more than they do Since these are known biases, we can easily correct for them

      4:27 PM - 8 Sep 2018
      • 1 Retweet
      • 8 Likes
      • Leesa 'soapy clean hands' Klich, MSc 🇨🇦 Janette Walton Todd Cameron AZ x2 💥💪 June Martin Tian Catherine Saxelby Linda Soars Rob England 🦓
      1 reply 1 retweet 8 likes
        1. New conversation
        2. Health Nerd‏Verified account @GidMK 9 Sep 2018

          The best part about issues like these is that they can actually make a study stronger! This sounds counterintuitive, but it is often true

          1 reply 0 retweets 2 likes
          Show this thread
        3. Health Nerd‏Verified account @GidMK 9 Sep 2018

          This is because issues that affect all groups in a study tend to bias the results towards the null hypothesis - i.e. they tend to make any results you DO find more significant

          1 reply 0 retweets 1 like
          Show this thread
        4. Health Nerd‏Verified account @GidMK 9 Sep 2018

          For example: if you measured calorie intake across 5 groups, and the 'true' result is 1. 1800 2. 2000 3. 2200 4. 2400 5. 2600

          1 reply 0 retweets 1 like
          Show this thread
        5. Health Nerd‏Verified account @GidMK 9 Sep 2018

          Now, generally speaking people underestimate their calorie intake. Let's say using this food questionnaire it is by 20% So the values you OBSERVE are: 1. 1440 2. 1600 3. 1760 4. 1920 5. 2080

          1 reply 0 retweets 1 like
          Show this thread
        6. Health Nerd‏Verified account @GidMK 9 Sep 2018

          In our first example - the TRUE value - the difference between each group is 200 In our second example - the OBSERVED value - the difference is only 160

          1 reply 0 retweets 1 like
          Show this thread
        7. Health Nerd‏Verified account @GidMK 9 Sep 2018

          So what's happened here is that the issues with our study have changed the results, thus biasing towards the null hypothesis

          1 reply 0 retweets 1 like
          Show this thread
        8. Health Nerd‏Verified account @GidMK 9 Sep 2018

          If we still manage to find a difference, it means that the difference is even more compelling An issue with our measurement is now actually making our results even more impressive!

          1 reply 0 retweets 2 likes
          Show this thread
        9. Health Nerd‏Verified account @GidMK 9 Sep 2018

          This is one of many reasons why epidemiology can be pretty confusing End thread

          0 replies 0 retweets 1 like
          Show this thread
        10. End of conversation

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