1. Lessons From The PEAR Lab
Exploring the Possibility of Anomalous Bias in Scientific Measurement

2. The Facts PEAR has shown that people can probably bias random machines
Through conscious intention (REG)
Through “subjective resonance” (FieldREG)

3. Do scientists influence measuring devices?
The Big Question
Do scientists influence measuring devices?

4. What We Need to Know Does anomalous bias exist? When does it happen?
How big an effect is it?
How can we detect it?
How can we prevent it?

5. The Sheep-Goat Effect
Many studies in parapsychology tell us that people who believe in ESP do better than chance on tests of ESP
Nonbelievers do same or worse than chance
It is possible (likely?) that this applies to effect seen by PEAR and thus to anomalous bias

6. The Sheep-Goat Effect Some Logic
Say the sheep-goat effect does not apply
Therefore all scientists anomalously bias their results
But measurement works just fine
So the effect must be small or nonexistent

7. The Sheep-Goat Effect More Logic Say the sheep-goat effect applies
We have no idea how big the effect is
As soon as scientists begin believing we’ll find out (it may be catastrophic)
Therefore PEAR needs to solve this problem or else stop publishing lest they convince their colleagues!!!

8. Scale
PEAR sees effects of a certain magnitude (roughly 2 bits off expectation per thousand bits)
This is at best an approximation for anomalous bias
Variable size?

9. Scale Any constant-size bias will eventually become a problem BUT!
There is reason to believe bias size will be smaller for more precise instruments
If this is so, there is no problem

10. Scale Say you measure some quantity to be n with experimental error σ
An additional measurement with error ≈ σ that is within (n - σ , n + σ ) does not tell you much
Small information gain → small reduction in entropy → small energy cost

11. Scale It appears that this effect is low energy
Thus one shouldn’t be able to bias measurements much farther than σ away from n
By the definition of σ, n is within a few σ’s of the true value, n0
So the bias size with respect to n0 should scale with σ as well

12. Detection (briefly) Volitional (like REG experiments)
Studies suggest changes in variance
Catch with statistics
Situational (like FieldREG experiments)
Can change direction, stop and start
Catch with REG array

13. Conclusion
Unless anomalous bias has a very specific set of properties, it will be a serious problem
This will be aggravated by the lack of time or distance dependence
Also perhaps by high stakes and large research groups

14. THAT IS TOO LIKELY TO IGNORE!
Conclusion
Even if combining the probability of PEAR’s effect being real with the chance that anomalous bias has bad properties yields a 1% chance of trouble
THAT IS TOO LIKELY TO IGNORE!

15. Lessons From The PEAR Lab
Exploring the Possibility of Anomalous Bias in Scientific Measurement