What does “correlation does not equal causation” mean, anyway?

You may have heard people respond to news from a recent study and say: “correlation does not equal causation.”

It’s a favourite line and has an important meaning.

The purpose of knowing the difference between correlation and causation is basically to put the study results in context and not give certain types of studies more weight than they deserve.

In a nutshell, “correlation does not equal causation” means that when two things happen at the same time—even though they seem related and it could make sense that one caused the other—it doesn’t necessarily mean that one caused the other.

So, let’s briefly chat about what those terms mean, and which types of studies show correlation and which show causation. Then I’ll share a few fascinating examples from the world of health research.

Study design defines why “correlation does not equal causation”

Science can be super-creative.  There are several types of studies used to conduct health research.

For a super-simple summary, check out my favourite infographic on the internet. This infographic shows some of the most common types of scientific evidence based on the type of study. Note the arrow on the left pointing down. You can see that the strength of the evidence increases as you go down this list. Here are those seven categories of evidence:

• Anecdotal & expert opinions
• Animal & cell studies
• Case reports & case series (observational)
• Case-control studies (observational)
• Cohort studies (observational)
• Randomized control trials (RCTs) (interventional/experimental)
• Systematic review

Let’s ignore the first two categories because they’re not done with groups of people and therefore have the lowest quality of evidence when it comes to human health or making credible health recommendations.

The next three types of studies are observational: case reports & case series, case-control studies, and cohort studies. The results of these observational studies can show correlations (not causation).

The next type of study, the randomized control trial, is considered to be a “gold standard” for causation because it is an experiment with an intervention (more on this below) designed to show differences before and after something is changed.

The last type, a review study (including systematic reviews and meta-analyses), is the strongest because it analyses the results of many studies to look for an overarching common conclusion.

Simply put, there are two main types of clinical research that can be done (you can see these in the parenthesis under the different study types in the link above):

• “Observational” where you look at what’s going on without making any changes/interventions, and
• “Experimental” where you make a change/intervention and see what happens.

Pro Tip: You can identify a study that’s designed to look for correlations because it asks people what they already do/did (observation), not what to change. These observational studies are simply collecting real world information.

Even within each study type, there really are a ton of different ways to collect data. And this is why many studies should be done before we can be sure of something. The higher quality, the better.

This is why the more studies we have on a particular topic, the more confident we can be. Having more studies, especially larger, longer, higher-quality ones can build robust body of evidence to make better health recommendations. This is especially true when different studies that look at the same thing in different ways all come to a similar conclusion. (You probably realize that sometimes studies that look at the same thing sometimes have different results! That’s OK. That’s where we have to scrutinize the type of study and how it was done to see which study’s results have more weight.)

In other words, strong evidence is needed to be sure of causation.

This brings us back to correlation and causation.

Correlation is

A connection or relationship between two or more things. (ref)

Essentially this means there’s a coincidence—two things coincide with each other. They may appear together or at the same time. They’re “associated” or “linked” with each other.

Links between two seemingly related things can be found everywhere in health science. These observational studies often observe people: what they normally eat/take/do, what other similarities they have, what lab results/conditions/diagnoses they have.

For example, in many nutrition studies, correlations happen when people who eat more or less of something (e.g., carbs, broccoli, vitamin D) may have higher or lower levels of a health effect (e.g., insulin resistance, ability to fall asleep, strokes). If the study was observational, we can’t be sure whether the food/nutrient caused the health effect, whether the health effect caused the food/nutrient intake/preference, or if there are other factors involved (e.g., underlying health issue, socio-economic status, food availability/preference, etc.) that led the food/nutrient to be correlated to the health effect.

In other words, the correlations between food/nutrients/habits and health effects may be because one caused the other, but observational studies can’t prove that one caused the other because participants were asked what they already do, not what to change.

These types of studies are very valuable to discover links between nutrition and lifestyle choices and risks of diseases. Every study that’s well-designed to answer a health question (observational or interventional/experimental) is another puzzle piece clarifying our understanding of the complex human body. So, correlational information, especially from larger or longer observational studies provide very valuable information. They might also justify funding for future, stronger (and much more expensive) experimental/interventional studies that are designed to prove causation.

Examples of health-related correlations (based on observing people without asking them to make any changes; so we can’t know what is the cause and what is the effect)

• This study followed 854 healthy adults to track their meals, snacks, and drinks, and compared that data to measures of heart health (blood triglycerides, blood sugar, insulin, HbA1c, etc.) . . . the researchers found very interesting strategies to healthy snacking! (Click here to share this study on strategic snacking with your audience)
• Some people who eat the MIND diet have a lower chance of developing symptoms of Alzheimer’s disease, even if they have harmful plaques and tangles in their brains (Click here to share this study on the MIND diet with your audience)
• A study followed 48,762 participants (nurses) over decades. The researchers found a trend linked to people who maintained good physical function and cognitive health, and were free from chronic diseases and mental illness! There is one macronutrient in particular that seemed to be linked to healthier aging. That macronutrient is protein. (Click here to share this study on protein for healthy aging with you audience)

These observational studies are often based on surveys of thousands, or even tens- or hundreds-of-thousands of people, sometimes over weeks, months, years, or even decades. These studies are valuable because they often collect so many data points from so many people.

But, because they ask people what they already do/did (not what to change and then measures before and after that change occurs), they can’t conclude whether the health or lifestyle caused the disease risk to go up or down.

Here is a website of spurious correlations. It shows how two things that are completely unrelated can appear together and be correlated to each other. My personal fav is the correlation between the popularity of the first name Tiara and Google searches for ‘why isn’t 11 pronounced onety-one’. 😂

Studies that show correlations are not designed to prove causation. They can’t look at cause and effect if they don’t look at before and after a change is made. Yet, they can provide very valuable insight into health and wellness.

We need a different type of study—one that has an intervention/makes a change—to see what the true causes are.

It’s not easy to prove that one thing caused something else to happen. Interventional studies are much more expensive to do and therefore are often much smaller and for shorter durations. This is one reason why they’re so few and far between when it comes to nutrition research. (We’ll talk about them in the next section.)

Causation is

The process of causing something to happen or exist. (ref)

Unlike correlation, causation is when you have objective confidence that, without one thing, the other would likely not happen. If A causes B, then without A, B would probably not occur. These are not coincidences. Causation is when there is fairly strong proof that one thing causes/is responsible for the other.

In health science, causation is determined with strong (and expensive) studies that test interventions. Basically, they measure something, make a change/intervention, and then measure it again to see what (if anything) changed after the change/intervention was made. One of these types of studies is considered to be the “gold standard” for testing treatments and is called a randomized control trial (RCT)

In an RCT, participants aren’t simply asked questions about what they already do/did in terms of their nutrition and/or lifestyles. RCTs randomly (the R in RCT) sort participants into two or more groups. One group called the “control group” (sometimes called the “placebo” group) doesn’t make the change/intervention being studied. Another group (the “intervention” group) makes the change or takes the treatment that’s being studied. Health data from both groups are monitored before any changes start, and again afterwards. The two groups are also compared to each other, to provide further evidence of causation if the impact happens only in the intervention group and not the control/placebo group.

This type of study provides stronger evidence that the intervention had an effect, and therefore caused any changes to their health.

Interventional/experimental studies are not very common for nutrition research because they’re much more expensive and more difficult to do. Plus, food, nutrition, and lifestyle are much more complex than just, for example, starting a new pill every day.

Examples of health studies that can determine causation (clinical trials that ask people to make a change and measures before and after the change is made)

• When people are stressed, their levels of the hunger hormone ghrelin increase (confirmed by measuring people’s ghrelin level, putting them in a stressful situation, then testing their ghrelin level again) (Click here to read a transcript where I chat about this study about ghrelin levels and download a free mini-article to share with your audience)
• Here is a blog post I wrote highlighting two randomized control trials done in the field of nutrition. I felt the need to write about these because there are so few interventional trials done in the field of nutrition (Click here to read about Dr. Hall’s metabolism research and Dr. Jacka’s nutritional psychology research)

How do observational studies show correlations/associations/coincidences/links (and *not* causes)?

Here are a few ways to do an observational study to find correlations, links, or associations:

• We look at people who eat a certain way (based on their answers to a questionnaire) and see what types of diseases/test results/symptoms they have. It’s like a “snapshot in time.” (These are cross-sectional observational studies.)
• Or we do it the other way around. We take a group of people with certain diseases/test results/symptoms, and then ask them which/how much different foods/drinks they’ve consumed. So, we’re looking backward (retrospectively) to see if we can figure out what they may have done that may be related in some correlational/associational way. (These are case-control observational studies.)
• Or, even better, we can take a group of people, ask them what they eat, and have them give blood/urine samples, or share their medical records over weeks, months, or even years (prospectively). Then, we see who gets which disease/test result/symptom over time. (These are cohort observational studies.)

You’ll notice that none of these ask participants to make any changes/interventions. They are free to continue living their lives as they otherwise would. No one is asked to eat a certain way for a certain time. No one starts taking a pill (active ingredient or placebo) or doing a certain amount of a certain exercise. No one is given dark chocolate to eat every day.

The information we gather from these types of studies are observations. Observations are great to know about, and sometimes when we look at observations, it may make total sense that one thing caused the other.

BUT,

Without at least one interventional study, many of these observations are later proven wrong with newer and better studies. This is why some popular health and nutrition claims are later disproven: because stronger studies have looked deeper and found more details that we didn’t ever know.

I like to think about it from the perspective of the gut-brain axis. Twenty years ago we had little, if any, evidence that it existed, let alone how strong it is. By staying up-to-date with newer and better research, over the years we’ve refined our knowledge with more understanding and can become an expert and make better health recommendations.

Why do we do “observational” studies if the results don’t prove causation?

First of all, correlations find links that may be very worthwhile to study in more depth and find out what causes something. Secondly, the more observational studies we have that look at the same thing from different angles and mostly see the same correlation, the more confident we can be that one of those things may cause the other. Each study that looks at something and tries to figure out what it’s linked to sheds light on another piece of the whole puzzle. The more pieces we have, the clearer the image gets.

Plus, some types of interventional/experimental studies are unethical, so the best information we can hope to have is based on several high-quality observational studies.

About observational studies, Julia Belluz at Vox says:

This study design can be very valuable — it’s how scientists learned about the dangers of smoking and the benefits of exercise. But because these studies aren’t controlled like experiments, they’re a lot less precise and [more] noisy.(ref)

How can experimental studies show causation?

Experimental (or interventional) studies do just that: They intervene in a process or someone’s life to do an experiment. They don’t just ask questions and take measurements, they require a change or intervention.

Whether that change is to take a pill (or placebo), use/stop using a certain product, or even be admitted to a “metabolic ward” and be given exact amounts of foods to eat (more on that below), it’s an intervention.

And when you intervene in someone’s life, to make that change, you can be more certain that it’s that change that caused an effect, and not just an association (or coincidence).

How? Because you can take measurements before and after the change.

Even better, when you have several experimental studies looking at the same thing in a different way and having similar results, this makes it pretty clear that there is a  cause-effect relationship.

This is where review studies come in. There are several different types of reviews, including the highest quality systematic reviews. What they have in common is that they take several studies that try to answer the same question, critically evaluate them, and come up with an overview of the topic.

PRO TIP: If you’re researching studies about a topic, start with the review studies. It is easier (and smarter, IMHO) to start your research with a smaller number of higher-quality studies (like reviews) before diving into clinical trials or observational studies.

Here’s what it looks like to search for review studies in PubMed:

Why should we do different kinds of studies?

Scientific knowledge builds on itself. There is a foundation of health knowledge that is based on thousands of studies done over the past 100+ years. In fact, there may be a bunch of different studies (of all different types) that point in the same direction. And that is pretty solid evidence.

So, before we would ever really change a health recommendation, many high-quality studies need to be done. Then, look at the results of those studies to see if they point to the same conclusion. All of that evidence—observational correlations and experimental causations—should be weighed to come to a solid conclusion.

Nutrition studies are usually observational

Because we simply can’t, and shouldn’t, force people to eat a certain way for a long time, nor could we change people’s diets or habits without them knowing (the “placebo effect” can also bias study results), nutritional science is very observational (“correlational”). I mean, how are you going to ensure that someone sticks to a very prescribed amount of certain foods day in and day out for weeks (or longer) to see if it affects their cholesterol or insulin levels?

It can be done, but it requires a lot of oversight and is expensive. This is why the few RCTs done in nutrition are small (done in a few dozen people) and short (a few weeks long). Of course, you can do this when it comes to supplements/placebos. But, when it comes to having people stick to a new diet or exercise or sleep plan—that’s really, really hard!

In this blog post I talk about two high-quality nutrition trials (RCTs):

• Dr. Hall’s metabolism research
• Dr. Jacka’s nutritional psychiatry research

These kinds of experimental nutritional studies are rare because it’s very hard to get people to agree to eat a certain way and have everything measured. They’re also very expensive. But, even if they’re small studies done on one or two dozen participants, one RCT provides stronger proof of a cause-effect relationship than one observational study.

If you don’t require a change (or intervention); how can you prove something actually caused something else (i.e. “causation”)?

Answer: You pretty much can’t.

Hence, “correlation does not equal causation.”

Conclusion

“Correlations” are associations/links that happen when two things occur at or near the same time. These results come from observational studies when we observe and measure things. We’re watching, learning, and doing some math to see which things are associated with each other because they seem to happen around the same time. Researchers ask people what they already do/did and how that affects them.

“Causations” are when the research is strong enough to show that one thing made something else happen. These results are much more robust to show that a change/intervention caused something to happen differently than it normally would. These results come from experimental or interventional studies. When we tell people what to change, then measure the before and after the change/intervention is made, we can be much more confident that the change/intervention is the cause of what happens.

Signing off and toasting: To rocking the “correlation does not equal causation” card.

Over to you

What do you think? Did I explain this uber-fascinating concept (or can I improve it)?

Are the differences between observational (that yield “correlational” results) and experimental/interventional studies (that yield “causational” results) clear?

Do you feel confident that you can rock the “correlation does not equal causation” card?

I’d love to know (in the comments below)!

Originally published Aug 2022, updated with more awesomeness June 2025.

Further reading

• A Rough Guide to Types of Scientific Evidence: Compound Chem
• Clearing up confusion between correlation and causation: The Conversation
• If ‘correlation doesn’t imply causation’, how do scientists figure out why things happen?: The Conversation
• Association is not the same as causation: Students 4 Best Evidence
• The Evidence-Based Medicine Pyramid!: Students 4 Best Evidence
• Calling Bullshit: Carl Bergstrom and Jevin West