Your life is an n-of-1 study
What the research says about weather, weekdays and mood: the effects are real but small on average and differ a lot between people, which is why personal analytics needs the same statistics that research uses.
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Notes from Daynostic
Statistics, privacy and the practical problems of tracking a life in numbers.
Do habit trackers actually work?
Self-monitoring is one of the best-evidenced techniques in behavioral science, yet most tracking apps fail in real-world use. What the research shows about that gap, and what it implies for design.
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Notes vs numbers: journaling vs statistics
Daynostic runs on numbers and journaling. The research on expressive writing, why free text cannot enter a correlation, and how notes and numbers play different roles.
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Recall bias: why memory of last month is unreliable
Memory does not store the weeks that passed; it reconstructs them, badly. The peak-end rule, duration neglect, and why clinical science switched to in-the-moment logging.
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Can you detect your own seasonal pattern?
Seasonal mood swings are real for some people and absent for most, and population research cannot say which applies to a given person. Why daylight, latitude and season cancel out on average, and why a year of personal data is the only way to find the signal.
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Your location never leaves your phone
How Daynostic collects weather on the device, through Open-Meteo, so coordinates are never sent to its servers.
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Sleep and mood: what averages hide
The research on sleep and next-day mood is real, directional and strongly individual. Why population averages blur it, why perceived sleep predicts mood better than the clock, and what a season of personal logs can show.
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How many days until the data means anything?
Why a pattern from a handful of logs is noise: what statistical power and sample size mean, how many observations are needed to detect an effect, and why an empty insights list at the start is the correct result.
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Correlation isn't causation
What a correlation in personal data can and cannot indicate: reverse causation, hidden confounders, and the Bradford Hill viewpoints science uses to judge when an association is worth believing.
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How Daynostic decides an insight is real
The statistical methods behind Daynostic's insights: Spearman rank correlations, permutation testing, false-discovery-rate correction, and controlling for day-of-week effects.
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