The 4 Cs – Causation, Correlation, Coincidence and Confounding Factors

I learn a lot in my private patient group. I also notice people jumping to conclusions on the cause of their issue when two variable things change at the same time. It is pretty normal (and even I do it) to want to know what is causing a symptom but working out the causes is not always easy. Treating the symptoms may be a more efficient use of time and energy. Healthcare professionals are better placed to work this stuff out but in 2025, the internet, google, and even artificial intelligence can help the resourceful patient/caregiver. But all of these have risks and hidden dangers for the unwary. My tip is that you need to be suspicious of the sources, even AI gets things wrong (thus why they all have disclaimers saying so). This can lead to stress which is not good for someone with a long term illness.

There is a longstanding medical adage “Correlation does not necessarily imply causation” is a warning that association between two variables (correlation) does not automatically mean one directly causes the other (causation). A true causal link means one variable directly influences another, while correlation simply means they change together. It’s also important to apply analysis of any ‘Confounding Factors’. This fallacy occurs because a third, confounding variable may be the true cause of both, or the relationship could be coincidental. Thus the title, ‘The 4 Cs’.

What is Causation

Causation means that one event directly leads to another. A key aspect of causation is that the cause precedes the effect in time.
A simple example: Cause: You forget to water your plants. Effect: The plants will wilt. It’s not a coincidence!
Instead of just Science aims to establish causation through rigorous testing and experimentation.

What is Correlation

Correlation indicates a statistical relationship where two variables change together.
However, correlation doesn’t automatically imply causation. The relationship could be coincidental, or both variables might be influenced by a third, unobserved factor.
For example, ice cream sales and drowning incidents are correlated (i.e. both increase in summer), but one doesn’t cause the other. Warm weather is a common cause. p.s. statistics also indicate that shark attacks rise at the same time as ice cream sales. Think about it.
Correlation is a useful tool for identifying potential relationships, but further investigation is needed to determine if a causal link exists.

What is Coincidence

Coincidence is when two events happen at the same time or in close succession by pure chance, without any logical or causal connection.
An example is the correlation between the number of non-commercial space missions and the number of sociology doctorates awarded – these are likely coincidental.
While coincidences can be interesting or even remarkable, they don’t indicate a deeper relationship between the events.

Confounding Factors

A confounding factor is a third variable that influences both the exposure (e.g., a treatment or behaviour) and the outcome (e.g. disease), creating a misleading association. I guess some might think that is a coincidence but it is not random like a coincidence; it’s a more structured problem. A confounding factor can cause an impact making the exposer look riskier or safer than it really is (unless properly controlled in study design or analysis).

One example in medicine.

Exposure → Outcome: The relationship we’re testing (coffee → heart disease).
Confounder → Exposure: The confounder influences who is exposed (smokers tend to drink more coffee).
Confounder → Outcome: The confounder also influences the outcome (smoking causes heart disease).

This triangle of relationships is what makes confounding dangerous — it can make the exposure look harmful (or protective) when the true driver is the third variable. i.e. smoking, not coffee, was the real culprit. Coffee only looked guilty because smokers tended to drink more of it.

Summary

Causation is about cause and effect e.g. it’s not always a NET related hormonal issue.
Correlation is about statistical association e.g. some NETs rarely metastasise, some frequently do.
Coincidence is about chance occurrences. Incidental findings and regular comorbidities are very common and much (much much much) more common than NETs.
Confounding factors can make the exposure look guilty and come to the wrong conclusionThe 4 Cs – Causation, Correlation, Coincidence and Confounding Factors.
Understanding the difference between these concepts is crucial for critical thinking and avoiding faulty reasoning (especially when interpreting data or making decisions). This are also applicable in cancer patient support groups to avoid unnecessary worry and stress.
Act cautiously before assuming that two simultaneous events are correlations rather than coincidences and that one must be a causative effect of the other. Do not cross the bridge until you get to it.
Understand the heterogeneity of NENs when in patient groups.

Disclaimer

I am not a doctor or any form of medical professional, practitioner or counsellor. None of the information on my website, or linked to my website(s), or conveyed by me on any social media or presentation, should be interpreted as medical advice given or advised by me.

Neither should any post or comment made by a follower or member of my private group be assumed to be medical advice, even if that person is a healthcare professional.

Please also note that mention of a clinical service, trial/study or therapy does not constitute an endorsement of that service, trial/study or therapy by Ronny Allan, the information is provided for education and awareness purposes and/or related to Ronny Allan’s own patient experience. This element of the disclaimer includes any complementary medicine, non-prescription over the counter drugs and supplements such as vitamins and minerals.