Design a sampling interval with competing error sources
Separate exact-function truncation, reading bounds and untested calibration assumptions.
Learning outcome: At epsilon=.04 compare h=.1,.2,.3,.5,1. Retain endpoint readings, the analytic teacher value and both error terms; choose an interval by the declared bound, not visual smoothness.
Starting recap
Differentiate or integrate with stated units, initial values, event boundaries and evidence limits.
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Try
At h=.2,epsilon=.04, truncation=.04 and reading bound=.20 m/s; absolute-error bound=.24 m/s.
Open the experimentPredict
Must the smallest sampling interval produce the most accurate derivative estimate?
Experiment
At epsilon=.04 compare h=.1,.2,.3,.5,1. Retain endpoint readings, the analytic teacher value and both error terms; choose an interval by the declared bound, not visual smoothness.
Balance differentiation truncation and reading bounds
Worked teaching model · synthetic values · Teacher generator x=t³−2t, centered at t=2 s. Each endpoint reading independently lies within ±epsilon. A displayed worst-case pair uses lower minus epsilon and upper plus epsilon. Bound epsilon/h combines with exact cubic truncation h²; not a probability/confidence interval or measured instrument.
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Synthetic reading pair
Axes rescale independently. Curves join sampled coordinates; a joining segment is not additional measured evidence. Read physical units rather than screen angles or apparent slopes.
Synthetic reading pair: numerical coordinates
| time (s) | position (m) |
|---|---|
| 1.8 | 2.192 |
| 2.2 | 6.288 |
Error bound across available widths
Axes rescale independently. Curves join sampled coordinates; a joining segment is not additional measured evidence. Read physical units rather than screen angles or apparent slopes.
Error bound across available widths: numerical coordinates
| half-width (s) | velocity error bound (m/s) |
|---|---|
| 0.1 | 0.41 |
| 0.2 | 0.24 |
| 0.3 | 0.223333 |
| 0.4 | 0.26 |
| 0.5 | 0.33 |
| 0.6 | 0.426667 |
| 0.7 | 0.547143 |
| 0.8 | 0.69 |
| 0.9 | 0.854444 |
| 1 | 1.04 |
| Quantity | Value | Unit |
|---|---|---|
| Lower-time adverse reading | 2.192 | m |
| Upper-time adverse reading | 6.288 | m |
| Adverse-pair derivative estimate | 10.24 | m/s |
| Exact-data centered estimate | 10.04 | m/s |
| Teacher analytic velocity | 10 | m/s |
| Positive cubic truncation | 0.04 | m/s |
| Reading-error bound | 0.2 | m/s |
| Estimate-error lower endpoint | -0.16 | m/s |
| Estimate-error upper endpoint | 0.24 | m/s |
| Conservative absolute-error bound | 0.24 | m/s |
Reducing the spacing shrinks truncation but amplifies a fixed position-reading bound.
Subtracting two readings each bounded by epsilon gives a worst-case numerator error 2epsilon; dividing by 2h gives epsilon/h. The cubic centered bias is exactly h². These are deterministic bounds, not independent Gaussian errors or proof that the displayed worst-case pair is typical.
Derive before checking. Retain contrasting captures and explain one assumption that could fail; calculated evidence is not actual laboratory work.
Reducing the spacing shrinks truncation but amplifies a fixed position-reading bound.
Subtracting two readings each bounded by epsilon gives a worst-case numerator error 2epsilon; dividing by 2h gives epsilon/h. The cubic centered bias is exactly h². These are deterministic bounds, not independent Gaussian errors or proof that the displayed worst-case pair is typical.
Captures preserve recipes and replay their calculated results. They are synthetic model evidence, not physical measurements.
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Could agreement with this generated truth certify a real instrument clock or an unknown trajectory shape?
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