#370 - AMA #76: Peter evaluates longevity drugs, aspirin for CVD, and strategies to improve muscle mass — proven, promising, fuzzy, noise, or nonsense?
This AMA episode with Peter Attia, MD, revisits the "proven, promising, fuzzy, noise, nonsense" scale for evaluating scientific claims. It defines these categories and sets the stage for applying them to topics like geroprotective drugs, low-dose aspirin, and muscle mass interventions.
Deep Dive Analysis
4 Topic Outline
Introduction to AMA #76 and episode topics
Defining the 'proven, promising, fuzzy, noise, nonsense' scale
The importance of 'strong convictions, loosely held' mindset
Applying the evidence scale to geroprotective drugs
6 Key Concepts
Proven (Scientific Claim)
A claim considered as close to well-established as possible, supported by extensive high-quality and consistent data, though nothing in biology is technically 'proven' like in mathematics.
Promising (Scientific Claim)
A claim with good supporting data, but which may still require replication or more consistent evidence across all studies before being fully accepted.
Fuzzy (Scientific Claim)
A claim with some data, but the evidence is inconsistent and incomplete, indicating poor overall data quality with only a potential signal for further investigation.
Noise (Scientific Claim)
A claim for which there are no real meaningful results, suggesting it's best to wait for more data rather than being distracted by it, hoping it might move up to 'fuzzy'.
Nonsense (Scientific Claim)
A claim that has been actively refuted by data, making it as close to disproven as possible in scientific terms, where the evidence goes against the initial assertion.
Strong Convictions, Loosely Held
A crucial attribute for great scientists and investors, meaning one has firm beliefs and puts capital or effort behind them, but remains flexible and willing to change them the moment new data emerges, prioritizing truth over being right.
2 Questions Answered
The categories are proven, promising, fuzzy, noise, and nonsense, representing a spectrum from well-established truth to disproven claims based on the quality and consistency of supporting data.
Great scientists and investors possess 'strong convictions, loosely held,' meaning they have firm beliefs but are willing to change them immediately when new data emerges, prioritizing truth over being right and avoiding doubling down on incorrect positions.
10 Actionable Insights
1. Embrace Strong Convictions, Loosely Held
Form strong beliefs based on available data, but be prepared to change them readily when new information or data emerges, rather than doubling down on old positions. This approach is crucial for both scientific integrity and successful investing, as it prioritizes knowing what is right over being right.
2. Prioritize Truth Over Being Right
Cultivate the mindset of a great scientist or investor by being married to knowing what is right, not to being right. This means following the data wherever it leads, even if it contradicts your existing hypotheses or beliefs.
3. Abandon Ideas When Facts Contradict
Be willing to ‘kill your babies’—your most beautiful hypotheses or cherished ideas—if they are categorically disproven by ‘ugly facts’ or new data. Clinging to incorrect beliefs can lead to ridicule in science or financial ruin in investing.
4. Evaluate Claims with P-P-F-N-N Scale
Apply the ‘proven, promising, fuzzy, noise, nonsense’ framework to assess claims. This allows for an apples-to-apples comparison of what is known about various topics and helps in understanding how to think about them.
5. Identify “Proven” Claims
Recognize ‘proven’ claims as those with lots of high-quality, consistent data, representing the closest to a well-established truth in biology, though nothing is technically ‘proven’ like in mathematics.
6. Assess “Promising” Claims
Classify claims as ‘promising’ when they have good supporting data, but may still require replication or more consistent results across all studies.
7. Treat “Fuzzy” Claims with Caution
Approach ‘fuzzy’ claims with skepticism, as they are based on inconsistent and incomplete data, even if there might be a weak signal present.
8. Avoid Distraction by “Noise”
Do not be distracted by ’noise’ claims, which lack meaningful results; instead, wait for more substantial data to emerge that might elevate them to a ‘fuzzy’ or higher category.
9. Discard “Nonsense” Claims
Dismiss ’nonsense’ claims, as these are actively refuted by existing data, making them as close to disproven as possible.
10. Recognize Fluidity of Understanding
Understand that the categories of ‘proven, promising, fuzzy, noise, nonsense’ are fluid, and claims can move up or down this scale as new information and data become available.
3 Key Quotes
A great scientist is not married to being right. They're married to knowing what is right and they're going to go wherever the data take them.
Peter Attia
You could have the most beautiful, beautiful hypothesis ever, and it can be categorically slayed by ugly facts.
Peter Attia
It's hard. It's hard to kill your babies.
Peter Attia