Question
Do not mistake frequent readings for independent evidence
Recording the same slow-moving process every second does not create a new world every second.
When it fits
- A time series produces many closely spaced observations and very confident statistical claims.
When to avoid it
- There is no universal conversion from raw rows to an effective independent sample count.
A question to ask
How quickly can the measured process genuinely change? · Do adjacent readings share a trend, cycle or disturbance? · Does the analysis account for that dependence and the observation spacing?
Why it matters
Inspect whether successive readings depend on earlier ones. Autocorrelation can make an independent-error calculation too optimistic about uncertainty. Preserve timestamps and use a method appropriate to the time structure rather than treating the row count as the amount of independent information.
An example
A room temperature logged every second contains many rows influenced by the same heating cycle.
Check your result
The uncertainty calculation reflects the dependence structure rather than only the file's length.
Keep this limit in mind
- There is no universal conversion from raw rows to an effective independent sample count.
Connected ideas
Useful withDistinguish variation in cases from uncertainty in a mean
Evidence and sources
Autocorrelation indicates relationships among observations across time and can invalidate uncertainty calculations that assume independent errors.
A larger number of closely spaced readings is not automatically a proportionate increase in independent information.
Autocorrelation · Lagged relationships; importance of checking randomness assumptions