Pomodoro Method Randomized Self Experiment

post by niplav · 2024-09-29T21:55:04.740Z · LW · GW · 1 comments

Contents

  Scoring the Market
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Value tracked	Effect size d (λ, p, σ change, k)
Productivity	0.26 (λ≈6.23, p≈0.069, 0.05, 52)
Creativity	-0.04 (λ≈0.58, p≈0.92, 0.01, 52)
Subjective length	-0.147 (λ≈3.33, p≈0.37, 0.03, 52)
Happiness	-0.07 (λ≈0.32, p≈0.96, 0.01, 111)
Contentment	-0.13 (λ≈1.08, p≈0.83, 0.05, 111)
Relaxation	-0.04 (λ≈1.23, p≈0.8, -0.25, 111)
Chastity	-0.14 (λ≈7.76, p≈0.02, 0.74, 111)

I ran the experiment from 2024-01-29 to 2024-06-17, using spt with this script, managed by this script.

The data on whether a particular day was a pomodoro-method day was saved in this file, and the data on the pomodoros was saved in this file.

The code for loading and transforming the pomodoro data isn't particularly interesting, if you're curious you can find it in this file.

datasets=get_datasets_pom()

Let's proceed to the analysis, then (using the same methodology as for my nootropics experiments:

res=analyze(datasets)

And the results are:

    >>> res
        productivity  creativity     sublen       happy     content     relaxed       horny
    d       0.259951   -0.041504  -0.147437   -0.073699   -0.132798   -0.038319   -0.144040
    λ       6.225107    0.583007   3.329000    0.318865    1.078502    1.232905    7.756272
    p       0.069062    0.918520   0.368416    0.959552    0.827240    0.795999    0.022903
    dσ     -0.050269    0.013871   0.033902    0.007177    0.047723   -0.252365    0.744675
    k      52.000000   52.000000  52.000000  111.000000  111.000000  111.000000  111.000000

I didn't meditate or do flashcards during that time, so most of the results don't contain any content.

So the pomodoro method somewhat increases productivity (at the edge of statistical significance), and maybe decreases subjective length of the day a bit.

Scoring the Market

As this experiment was done in the context of an endeavour of selecting QS experiments via prediction markets, I can now score the market:

def logscore(o,p):
        return np.mean(o*np.log(p)+(np.ones_like(o)-o)*np.log(np.ones_like(p)-p))
p=np.array([0.06, 0.29, 0.39, 0.19, 0.07])
o=np.array([0, 0, 1, 0, 0])
logscore(outcomes, p)
-0.3258531953347593

Honestly: The market did pretty well.

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