This is the practical side of the controlled variables you named in your design. The goal is to minimise systematic errors — the kind that consistently shift your results one way — so that any effect you measure genuinely comes from your independent variable. Keep everything else constant and you get valid results and a fair test.
📚 What you need to know
Controlling variables minimises systematic errors and ensures a fair test
Calibrate instruments against a known standard (e.g. zero a balance, check a thermometer in ice/boiling water)
Insulate thermal experiments to reduce heat loss or gain
Reduce friction and electrical resistance where they distort results
Account for background radiation in experiments with radioactive sources
Why control variables?
The main goal is to minimise systematic errors — errors that consistently push your readings in one direction (a mis-zeroed instrument, steady heat loss). By holding all other factors constant, you can be confident any effect you measure is due to your independent variable, your results are valid, and you’re running a genuine fair test.
Calibrating measuring apparatus
Calibration is checking an instrument’s readings against a known, reliable standard and adjusting it if necessary. It’s how you make sure your data is accurate before you even start.
Sensors: zero a force sensor with no load; check a motion sensor against a known distance.
Digital thermometers: verify against boiling distilled water (100.0 °C at standard pressure) and crushed ice (0.0 °C).
Digital balances: always zero (tare) before use, so you measure only the object’s mass.
Ammeters and voltmeters: check they read zero with no current or potential difference.
Calibration is one of those steps that feels trivial but quietly protects your whole data set. If your balance reads 0.002 kg with nothing on it, every single mass you record is wrong by the same amount — a textbook systematic error. Thirty seconds of zeroing and checking against a known standard removes it entirely.
Maintaining environmental conditions
The lab itself can affect your results. Key conditions to control include temperature, pressure, humidity, air currents (draughts), and light intensity.
Temperature is often the most important, because it affects density, gas pressure, and electrical resistance. Draughts from windows or air conditioning can cool a substance and ruin a calorimetry experiment — control them by closing windows or using a draught shield. When a variable can’t be perfectly controlled (like ambient room temperature drifting slightly), the best practice is to monitor and record it, then discuss its impact in your evaluation.
Insulating against heat loss or gain
In any thermal experiment — specific heat capacity, for instance — the biggest source of error is unwanted heat exchange with the surroundings. Insulating the system is crucial for accurate temperature data.
A polystyrene cup (a good insulator) inside a beaker, with a lid to cut evaporation and convection, keeps heat loss to a minimum for accurate temperature readings.
Common techniques: use a polystyrene cup instead of a glass beaker (far better insulator), sit it inside a larger beaker for an insulating air layer, and add a lid with holes for the thermometer and stirrer to cut heat loss by evaporation and convection.
Reducing friction and resistance
Friction (mechanics experiments): reduce it by lubricating moving parts, adding bearings, or using low-friction apparatus like an air track, which floats gliders on a cushion of air for near-frictionless motion.
Electrical resistance (circuits): reduce unwanted resistance by using shorter, thicker wires, keeping the current low, and ensuring connections are secure — poor connections add extra resistance.
Accounting for background radiation
In experiments with radioactive sources, background radiation must be accounted for. Using a Geiger–Muller tube, first record a background count rate with no source present, then subtract that background from every subsequent measurement — so you’re only measuring radiation from the source itself.
EXAMPLE
Controlling variables in the oscillation investigation — how to keep it a fair test.
Amplitude of the swing
Release from the same small angle (< 10°) each trial — keeps the small-angle approximation valid, avoiding a systematic error.
Friction at the pivot
Use a smooth knife-edge pivot or glass tube — minimises the damping that would remove energy.
Air resistance
Work indoors away from draughts, with a dense, aerodynamic bob — keeps damping negligible and consistent.
Each control tied to a specific physical reasonNotice every control comes with a justification. “Keep the angle small” isn’t enough — it’s “keep it small BECAUSE the theory relies on the small-angle approximation.”
💡 Top tips
Be specific: “an air track provides a near-frictionless surface,” not “reduce friction.”
Always calibrate/zero instruments before use.
If a variable can’t be controlled, monitor and record it instead.
Justify each control — explain why it matters for validity.
Focus your effort on the variables with the biggest impact.
⚠ Common mistakes
Vague controls (“I’ll reduce friction”) with no method
Forgetting to calibrate or zero instruments
Ignoring background radiation in nuclear experiments
Overlooking simple controls (same equipment, same person reading)
Listing controls but never justifying why they matter
Quick recap: Controlling variables minimises systematic error and keeps the test fair. Calibrate instruments, hold environmental conditions constant, insulate thermal experiments, reduce friction and resistance, and subtract background radiation. Above all, be specific about how you control each variable and justify why it matters.
That completes Stage 1 of the scientific inquiry cycle — you can now explore a problem into a sharp question, design a valid investigation, and control the variables that would otherwise spoil it. These aren’t just IA skills; they’re how a physicist thinks about any experiment. Next in the cycle comes collecting, processing, and analysing your data — where all this careful planning finally pays off.
Variable control letting your data down?
Book a free meeting and we’ll pin down specific, justified controls — calibration, insulation, fair testing — so your investigation produces clean, trustworthy results.
