6.4 - Implementation and Measurements

Evaluate an experimental method by linking a specific weakness to a practical modification and then explaining how the change improves the measurement. The causal link matters more than naming sophisticated apparatus.

Discuss limitations as well as benefits. A light gate removes human reaction time, for example, but its trigger position or the part of an object that breaks the beam may still create uncertainty. A video helps only if its frame rate and scale suit the event.

Quantify the advantage when possible: a finer resolution, faster sampling, simultaneous readings or a longer timing interval should reduce a named uncertainty or prevent a named bias.

Judge the number of readings from the intended conclusion. There must be enough independent-variable settings to reveal the shape or key feature of the relationship, and enough repeated readings where random scatter affects each setting.

If a resonance curve has only one or two readings between $1.4$ and $1.6\,\mathrm{Hz}$, the peak frequency is poorly located even if many points exist far from the peak. More readings should be concentrated around the maximum.

Use the actual table or graph: count the distinct values, inspect their spacing and note whether repeats are shown. Explain what additional readings would make the conclusion more secure.

The range is the interval from the smallest to the largest value of the independent variable. Evaluate whether it is wide enough to expose the predicted change while remaining within safe, measurable and physically valid conditions.

A spring investigation using extensions only from $1.0$ to $1.5\,\mathrm{cm}$ may show too little force change to judge proportionality. Extending the interval can clarify the trend, but only while the spring remains below its elastic limit.

State a justified new minimum or maximum when the context permits, not merely 'use a wider range'. The proposed extension should target the weak evidence without changing the physical question.

A results table must not imply more precision than the apparatus and input data support. Check raw readings by instrument resolution and calculated values by the precision of the measurements used.

If measured values $3.4$ and $1.26$ are multiplied, the calculator gives $4.284$, but $3.4$ has only two significant figures, so reporting $4.3$ is appropriate. Writing $4.28400$ would invent precision.

Scan each column for one entry with extra or missing digits, then use the heading, instrument detail and neighbouring entries to decide the correction. Keep leading zeros non-significant: $0.00450$ has three significant figures.

Identify a unit by tracing what quantity was measured or calculated. A table heading should normally separate the quantity symbol from its unit, and every derived or transformed column needs the corresponding derived unit.

Convert prefixes consistently before calculation: $1\,\mathrm{mm}=10^{-3}\,\mathrm{m}$, so $1\,\mathrm{mm^2}=10^{-6}\,\mathrm{m^2}$. Squaring a value also squares its conversion factor.

To amend an incorrect heading, first identify the underlying quantity and transformation, derive the unit algebraically, then rewrite the unit without changing the numerical data unless a unit conversion is also required.

An inconsistent reading is one that does not follow the pattern of the other results beyond their normal scatter. Locate it in the table or as a point displaced from the graph trend, then investigate before deciding how it should be treated.

If a repeat under controlled conditions agrees with the surrounding trend and not the original value, record the original as anomalous and justify excluding it from a mean or fit. If the unusual value repeats, retain it and reconsider the assumed relationship or method.

Judge inconsistency relative to scatter and measurement precision. A point slightly off a best-fit line is not automatically anomalous; experimental data are not expected to lie exactly on a mathematical line.