r = 0.38 between tip_usd and surge_multiplier

Measured on ride-hail-trips: the Pearson correlation between tip_usd and surge_multiplier over 8,000 rows is 0.38.

That is weak and positive. Squared, it says the linear relationship accounts for 14.8% of the variance in either column. That is the number worth quoting rather than r itself: 0.38 sounds like a much stronger claim than 14.8% does.

df[["tip_usd", "surge_multiplier"]].corr(method="pearson")
# also worth running:
df[["tip_usd", "surge_multiplier"]].corr(method="spearman")

Run Spearman next to Pearson every time. They agree when the relationship is roughly linear and diverge when it is monotone but curved — and the gap between them is a free diagnostic that costs one line.

A correlation this small is compatible with a real relationship that is not linear, with a real relationship confined to one segment, and with nothing at all. It does not distinguish between them, and neither does a bigger sample.

Look at the scatter before quoting r. Anscombe's quartet is four datasets with identical correlation coefficients and nothing else in common, and the correlation explorer will draw this pair so you can see which case you are in.

Longer treatment in Correlation, confounding, and Simpson's paradox.