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Decoding Research

Decoding Research

Update: 2024-10-14
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Hey Team!

We’re diving right back into the world of ADHD research, continuing on from what we were talking about a few episodes back. In this episode, we’re going to be more focused on what goes into making ADHD research reliable. I go in-depth into what you can expect to find when reading a study and then also into what thing to look out for when trying to determine what’s really going on in those studies.

We’ll discuss how to navigate the sometimes confusing world of peer-reviewed journals, why sample sizes matter, and what to watch out for when it comes to conflicts of interest (I mean, everyone is interested in how ADHD research is funded, right?).

This piece was also initially going to cover misinformation, but with how much ended up going into everything else, I’m saving that for next week.

If you’d like to send me a question answer on the show feel free to head over to hackingyouradhd.com/contact and click the orange button

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Find the show note at HackingYourADHD.com/198

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This Episode's Top Tips

    1. The first step in examining a research paper’s credibility is to ensure that it is peer-reviewed. The peer-review process will cover many of the other steps that we discussed in this episode. While there is value in some non-peer-reviewed work, it’s important that we approach it with a skeptical lens.
    2. However, with that first tip, we should also know that we shouldn’t view peer-reviewed journals as a beginner’s source. They are written with the expectation of other experts as the intended audience, and without the prerequisite knowledge, it can be easy to misinterpret what is being said.
    3. One of the largest concerns about ADHD research comes from worries that pharmaceutical money will introduce bias into many of the studies; however, most research is actually funded through government grants, and the peer-review process is designed to help identify conflicts of interest and eliminate any bias that may be present.

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Decoding Research

Decoding Research