This idea says that a smarter math method can help scientists better tell when a workout doesn’t work — not just when they don’t have enough proof either way.
Mechanism
Synthesis from 1 study
When scientists measure muscle growth or strength, there's always some natural variation. This method doesn't just ask if there's a difference — it calculates how likely it is that any difference is too small to matter. That way, they can say for sure when something truly has no practical effect,...
Most probable mechanism
When measuring changes in muscle growth or strength, there's always some natural variation in how people respond. Instead of just asking if the change is big enough to be considered real, this method calculates how likely it is that the true effect falls within a range that doesn't matter — like whether a tiny difference in arm position actually affects muscle growth. This lets scientists say for sure when an effect is truly absent, not just when they didn't have enough data to notice it.
Measurement of physiological outcomes such as muscle size or force production contains inherent variability due to biological noise and measurement error
Statistical models incorporate prior knowledge and observed data to generate probability distributions over possible effect sizes
A predefined range of trivial effect sizes — where differences are too small to be meaningful — is used to evaluate the probability that the true effect lies within it
The ratio of evidence supporting a meaningful effect versus a trivial or null effect is computed to determine the strength of conclusion
Evidence from Studies
Supporting (1)
Community contributions welcome
The effects of shoulder extension angle on elbow flexor hypertrophy in the cable curl exercise
Contradicting (0)
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Gold Standard Evidence Needed
According to GRADE and EBM methodology, here is what ideal scientific evidence would look like to definitively prove or disprove this specific claim, ordered from strongest to weakest evidence.