Does anyone have any resources, twitter links, YouTube videos, online courses, about how to learn to scientifically and objectively research and check scientific claims that are made?
Ivor Cummins (Fat Emperor), and Zoe Harcombe seem to be very very good at this, and I’m looking for other critical thinkers to learn from.
I’m fed up of accepting old wives tales and anecdotes as fact.
Now if you excuse me, I have three boiled eggs to eat, just as soon as I can check the two egg rule is wrong.
I don’t know if there is anything simple out there. For me it was a full semester course. You need to be able to read and understand the middle of the article, not just the hypothesis. It’s about looking at who funded it and if they listed any conflicts of interest. See if the sample size and method makes sense for what they’re looking at. For example, the nurses health study at Harvard, every couple of years we get a study that asks “in the last year how many times a week did you eat_______” I’m likely to give way different answers based on season. In the late summer I eat cucumbers daily, not in the winter, so how many a week does that average out to? And people lie, so any study that is self reported is suspect. A medication study funded by the manufacturer or competitors is suspect. Diet studies funded by any food producer or manufacturer is suspect. In other words, most of them are suspect. I saw a study with a large sample size that reported that 50℅ more people in the study group had heart attacks. The truth was 3 people in the study group, 2 in the control. Funded by a competitor. The headline was true, but not statistically significant. And those things are just the beginning. You also need to see the actual paper copy of the journal to see who the advertisers are. Back in the 1960s, drug companies told the journals that they would pull advertising if they continued to publish Linus Pauling’s vitamin C research. That’s why so much crap can be found in peer reviewed journals.
I would really recommend Ben Goldacre’s excellent book “Bad Science”. For a flavour, his website is here:
He’s awesome!
My go-to resource for diet/science questions is Mark Sisson’s Daily Apple.
Keto friendly site. Good search function. Rarely disappointed.
I just bought the kindle version of his book, and am wathing his videos. Cheers.
btw, is that the VS2010 logo you are using for your avatar?
First a hypothesis is created. Like, when ice cream sales are high, the weather is hot. Therefore, my hypothesis is: selling more ice cream makes the weather hot.
So, now, let’s “test” the hypothesis. To do this, let’s determine under what conditions the hypothesis works, and where it fails.
Test #1) let’s look at all the hot days in 2016. What were the ice cream sales? Were they all high? Yes, they were. Ok. So far hypothesis looks good.
Test #2) let’s look at the difference between weekend ice cream sales, versus Monday to Friday ice cream sales in 2016, during the hot days. Hmmm. Something interesting in the data. Weekends tend to have more sales? Hypothesis starts to get a bit challenged. Seems like there is another factor influencing the behaviour.
Test #3) let’s look at ice cream sales on weekends only, and see how they correlate with temperature. Oh my. Wait a minute. Holidays during weekends that fall on cold days, like Easter, shows spike in ice cream.
Test #4) let’s look at hot food versus cold food consumption during the months of the year. Hmmm. Interesting. More soup during winter months, and more ice cream during hot months.
Test #5) let’s look at ice cream sales north versus south of the equator. Hmmmm. Interesting. The summer and winter months are opposite, north versus south of the equator. And where we see summer months, we observe higher ice cream sales.
So, I realize the testing above seems ridiculous, but this is the process of discovery…as in testing the hypothesis. The problem with bad science is when testing is rigged or customize to “prove the hypothesis”. What you really need to do is find the conditions that prove AND disprove the hypothesis.
I had the VB/VS6 logo as my Twitter avatar for a long time (not the same username as on here) , I listen to DNR / Hanselminutes / RunAs etc too.
I miss my .NET programming, all R and Python now though I still “get” to teach VBA on occasion. I find I can’t keep up in my current field and keep up in all of my languages too so some have to go. Good riddance WPF and ASP but I miss C#.
Aah, VBA, happy days. Word script. Remember that? the pre-cursor to VBA in Word?
I never got into ASP commercially, but I am working on an ASP.Net webforms app at the moment. I also dabbled with MVC and Angular to try to get some experience in that area too.
You’re right about it being hard to keep up. As I listen to episodes of DNR I sometimes wonder how most of these people keep up with all the things they talk about.
After several years of reading different information sources, my research distilled down to looking for high quality basic research, preferably more than one study. Along the way I have realized that there are so many myths and dogmas in all areas of medical sciences, it’s simply better to check for yourself in all important areas.
I use either PubMed or Google Schooar to locate relevant papers. A lot of time can be spared if you know the terminology used by researchers when giving a title to their papers. It’s also very useful to have a general idea of what options might pop up.
I coded a couple languages 40 hours a week at work, 4 hours every night at home, and my roommate and I coded various things about 10-12 hours a day every weekend for a couple years. Really loved coding. Then I got married, 60 hr a week job, 2 kids, now I just try to keep my SQL skills fresh and use R and Python. Always wonder where I would be if I had kept coding for those 10 years. Probably dead or tired of coding at the very least.
In addition to the aforementioned book Bad Science, Ivor Cummins mentioned these two sources of wisdom on a recent podcast:
https://www.amazon.com/Doctoring-Data-medical-advice-nonsense-ebook/dp/B00TCG3X4S
