Null Results: Why 'Nothing' Matters in Nutrition Research

Most nutrition research studies are designed to see what will happen if we change something. 

• What happens to muscle synthesis if we add more protein to the diet?
• What happens to cholesterol levels if we increase vitamin E intake?
• What happens to blood sugar levels if we decrease carbohydrate intake?
• What happens to immune function if we add more vitamin D?

Sometimes, of course, nothing happens. We often refer to that as a null result. And it’s easy to see a null result as a failed experiment. But actually, it’s not.

Joining me today to talk about null results is Greg Lopez. Greg is the lead scientific editor at examine.com, where he and his team collect, assess, and summarize a staggering amount of nutrition research. Below are some highlights from our conversation but please click on the audio player to hear all the juicy details.

Monica Reinagel: Examine.com is one of my go-to resources for nutrition research. Not long ago, you added a new section to the Nutrition Examination Research Digest (affectionately known as NERD), dedicated to reporting null results—or research where nothing happens. Why did you feel that this was important to shine a light on?

If there are strong null results, then people can avoid wasting their time and money on ineffective nutrition and supplementation.

Greg Lopez: People are turning to us in order to find out what works and what doesn't. And if there are strong null results, then people can avoid wasting their time and money on ineffective nutrition and supplementation.

MR: You mention the value that this might have for consumers who are trying to decide whether or not to try something. But I would think that for researchers, it would also be important to know about null results that have already been found so that they can focus their research attention more effectively.

GL:  Exactly. It's a big deal for researchers for a few reasons. When you have a whole bunch of small studies, you may see some null results because the studies are too small to catch small-size effects. But if you put all these results together into a meta-analysis, you can see more subtle effects. But if only positive results are published, then you're getting a biased snapshot of what the literature is actually saying.  So meta-analysis conclusions won’t be as reliable as they would be if more null results were published.

Ruling out ideas is how science works. If you already knew what the result was going to be, then you wouldn't need to do the experiment!

Plus, there's a lot...

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