Conducting¶
Syllabus mapping
Working Scientifically outcome — Conducting. Covers safely and accurately following (or adapting) a method, systematically recording data and observations, and identifying anomalous results as they occur. Like every Working Scientifically outcome, it's assessed through application inside a focus area's practicals and depth study, not as standalone content.
Outcome codes: CH-11WS-03 (Year 11) and CH-12WS-03 (Year 12) — Conducting scientific investigations. Same outcome wording in both years, with a separate code for each. Wording on this page is paraphrased for teaching use — check the official syllabus before treating any line as verbatim NESA text.
You need to know: how to record raw data (and observations) properly the first time, so you're not reconstructing it from memory afterwards, and how to recognise a genuine anomaly without quietly deleting inconvenient results.
Core¶
Recording data as you go¶
A raw data table should be built so that someone who wasn't in the room can read it correctly:
- Units go in the column header, once — "Mass (g)", not a unit repeated in every cell.
- Decimal places should match your instrument's precision, consistently — if the balance reads to 0.01 g, every mass in that column should be recorded to 0.01 g, not a mix of 2 g and 2.41 g.
- Record what the instrument actually showed, not a rounded or "tidied" version of it. If the balance reads 2.38 g, write down 2.38 g — round later, during Processing, not now.
- Record repeats as separate columns/rows, not just a running average in your head — you need the individual trial values later to check consistency and calculate a proper mean.
- Record qualitative observations too — colour changes, fizzing, a precipitate forming, the mixture getting warm. In chemistry, observations are often the evidence that a reaction happened at all.
Worked example — good vs poor raw data table
Timing the disappearing cross at 30 °C, three trials.
Poor:
| Trial | Time |
|---|---|
| 1 | 34s |
| 2 | about 36 |
| 3 | 35.2 |
Problems: no unit in the header, inconsistent decimal places, "about 36" isn't a measurement, and the actual temperature wasn't recorded.
Good:
| Trial | Starting temperature (°C) | Time for cross to disappear (s) |
|---|---|---|
| 1 | 30.5 | 34.2 |
| 2 | 29.5 | 35.8 |
| 3 | 30.0 | 35.1 |
Every value has the same number of decimal places, the header carries the unit once — and the actual temperature of each mixture is recorded, not just the water-bath setting. That's a deliberate method choice: it lets you check later whether variation in the times is explained by variation in the temperature.
Why repeats matter¶
A single measurement can't tell you whether it was typical or a fluke. Repeating each trial (see Planning) and looking at the spread of results tells you how much random variation exists in your setup — small, tightly-clustered repeats suggest a precise setup; widely scattered repeats suggest something (technique, equipment, conditions) is introducing significant random error. How you turn a set of repeats into a single reported value — and how you quantify that spread — is covered in Precision and Uncertainty; at the conducting stage, the job is simply making sure every individual repeat gets recorded.
Recognising an anomaly¶
An anomaly (or outlier) is a result that doesn't fit the pattern of the other repeats at the same IV value. Spotting one while you're still at the bench — not three days later while writing up — is what lets you investigate and, if justified, redo that specific trial.
A result is reasonable to flag or exclude only if you can point to a specific, identifiable cause observed at the time — for example, the bung popped out of the flask and gas escaped, or the solution wasn't stirred. "It didn't match what I expected" is never a valid reason to discard a data point. If you can't identify a specific cause, the honest move is to keep the value, flag it clearly as an outlier in the table, and deal with it openly during analysis rather than quietly removing it.
Worked example — flagging, not deleting
| Trial | Time for cross to disappear (s) |
|---|---|
| 1 | 35.2 |
| 2 | 34.8 |
| 3 | 51.3 (anomaly — a different student judged the endpoint, viewing from the side; excluded from mean) |
| 4 | 35.0 |
The anomalous trial is still visible in the table with the reason stated, and a fourth trial was run to replace it rather than reporting a mean of only two values.
Working safely and honestly¶
Two things that belong together at this stage, even though only one of them is usually framed as "safety":
- Follow the controls from your risk assessment (Planning) as you go — a risk assessment that gets written and then ignored during the actual prac hasn't done its job. Dispose of chemicals the way your teacher directs, not down the sink by default.
- Report what actually happened. Data doesn't get adjusted, rounded in a hypothesis-friendly direction, or invented for a trial you ran out of time to do. A result that contradicts your hypothesis is not a failed experiment — a hypothesis being wrong is a legitimate, useful scientific outcome, and Analysing is where you're expected to discuss why a result didn't match a prediction, not paper over it here.
Advanced
Noticing error type while you're still collecting data. You don't need the full mathematical treatment (that's in Precision and Uncertainty) to start noticing, at the bench, whether a setup seems to have mostly random error — repeats scattered fairly evenly around a central value, e.g. from judging when a cross disappears — or a systematic error — every repeat consistently high or low in the same direction. Common chemistry examples of systematic error: a balance that wasn't zeroed (tared), reading a meniscus from above instead of at eye level (parallax), a thermometer bulb not fully in the liquid, or a gas syringe that sticks. Spotting one during conducting is valuable because it's often fixable mid-experiment in a way it isn't once you've packed up.
Video/visual resources¶
- 🎥 TODO: source a short video on reading a meniscus correctly and using a balance (zeroing/taring, weighing by difference).
Check yourself¶
-
A student records a table of masses as: 2.4 g, 2.41 g, 2 g, 2.38 g, using a balance that reads to 0.01 g. Identify what's wrong and rewrite it correctly.
Answer
The decimal places are inconsistent — every value should be recorded to 0.01 g to match the balance — and the unit should be in a column header, not repeated. Corrected: a column headed "Mass (g)" with values 2.40, 2.41, 2.00, 2.38.
-
A student collects these five volumes of hydrogen gas from identical trials: 48 mL, 47 mL, 49 mL, 31 mL, 48 mL. They discard the 31 mL reading because "it's obviously wrong." Is this acceptable practice? What should they do differently?
Answer
Not acceptable as described — "obviously wrong" isn't a stated cause. It's acceptable to exclude if the student can point to something specific observed at the time (e.g. the bung wasn't pushed in firmly and gas was seen escaping). Without an identified cause, the correct approach is to keep the value in the table, flag it as an outlier, and address it in the analysis — or repeat that trial and record both results.
-
Explain why weighing out about 1.00 g of a solid gives a more reliable result than weighing out about 0.10 g on a balance that reads to 0.01 g.
Answer
The balance's uncertainty (about ±0.01 g) is the same size whatever mass is on it. For 0.10 g that's about 10% of the mass; for 1.00 g it's only about 1%. Using a larger mass makes the same fixed uncertainty a much smaller fraction of the measurement.