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Curious: What grows between a fire escape and a sunny ledge?

Playful: Your balcony just adopted a basil plant that refuses to share.

Practical: One pot, soil mix, and a seed packet get you started this weekend.

Curious | Playful | Practical

Known facts: Weekend sales rose. A promotion ran. Weather was good. These co-occurred; nothing else is observed.

Hypotheses only: Promotion drove the lift; weather did; they interacted; or a third factor (payday, event, seasonality) did. Correlation ≠ causation.

Data to distinguish: Historical weekend sales with promo/weather combinations (both, promo-only, weather-only, neither). Add controls (day-of-week, holidays, competitor activity). Run difference-in-differences or OLS:

import statsmodels.api as sm
# df: sales, promo (0/1), weather (score), controls
X = sm.add_constant(df[['promo','weather']])
print(sm.OLS(df.sales, X).fit().summary())

Compare coefficients and interactions; if promo remains significant after weather/controls, it is more likely causal. Randomized promo timing would be strongest.

Promise.all short-circuits: it fulfills only if every promise fulfills, otherwise it rejects with the first rejection (successful results are lost).

Promise.allSettled always waits for every promise to settle and always fulfills with an array of {status, value/reason} objects, so both successes and failures are preserved.

Example (keep successful data even if some fetches fail):

const urls = ['/ok', '/fail', '/ok2'];
const results = await Promise.allSettled(
  urls.map(u => fetch(u).then(r => r.json()))
);
const goods = results
  .filter(r => r.status === 'fulfilled')
  .map(r => r.value);
// goods still contains the two successful payloads

(If you used Promise.all the whole call would reject and goods would be empty.)