ACARA v9 CONTENT DESCRIPTION “select and use equipment to generate and record data with precision, using digital tools as appropriate”
Builds on planning a reproducible investigation. With a clear question and method in place, the next skill is generating the data itself: selecting an instrument with the right resolution, using a digital tool to record at even moments, and reading every value with precision so a real pattern can be trusted.
Select the instrument and match its resolution to the change
Some salts release heat as they dissolve, an exothermic change that warms the water around them. To follow that warming you select a digital thermometer rather than a guess by hand. The instrument must have the right resolution: if the reaction shifts the temperature by only a few degrees, a thermometer marked in tenths of a degree reveals the change, while one marked in whole degrees would hide it. Precision means reading to the finest sensible scale the tool allows, recording 24.3 degrees rather than a vague roughly 24.
A dissolving salt warms the water, logged each minute
A data logger recorded the temperature of water in a beaker as a salt was stirred in and dissolved. Switch between the table and a graph to see the readings.
Logging at equal one-minute intervals keeps the spacing even. The water warms quickly as the salt dissolves, peaks near 30 degrees, then begins to cool, a curve only visible because each value was recorded precisely at a regular moment.
Let the digital tool record at even moments
A data logger captures each reading at a fixed interval, evenly spaced and steady, free of a slow or distracted hand. As the values arrive they are kept in order beside the moment each was taken, so nothing is lost. If a reading looks odd, it is recorded honestly and noted rather than erased. Repeating the run is part of careful work too: the same salt dissolved on three separate days gives close but not identical results, and recording the real values lets you average them honestly instead of pretending they all agreed.
Three repeats of the peak temperature reached
Careful work means repeating. The same mass of salt was dissolved on three separate days, and the highest temperature reached was logged each time. Compare the readings.
The three peak readings are close but not identical: 30, 29 and 31 degrees. Recording the real values, rather than rounding them all to 30, shows the small natural spread and lets you average with honesty.
Spot the careless reading
Even with a digital tool, a single careless moment can spoil a record. If the probe is lifted out of the liquid for a second, or a value is mistyped, one point will sit well off the smooth heating curve. A reading that contradicts the steady trend of the others is the cue to check the probe and the entry on the spot, while the run is still going, rather than after the equipment is packed away.
A logged heating curve: find the careless point
A salt was dissolved while a logger recorded the temperature each minute. One value does not fit the smooth rise, most likely a careless reading.
Click the point that does not fit the pattern of the others.
Which habits give precise, trustworthy data?
Generating good data is a set of deliberate choices about equipment and method. The claim is that a particular routine produces precise, trustworthy measurements. Sort each habit by whether it genuinely supports that claim, or whether it would damage the quality of the data instead.
Judge the measuring routine
The claim: this routine generates precise, trustworthy data. Decide which habits support it.
Claim: This routine produces precise and trustworthy temperature data.
A calibrated digital thermometer reading to a tenth of a degree is selected for the small change expected.
The data logger records the temperature automatically at a set interval.
A point that looks too high is deleted so the curve stays smooth.
The probe is kept fully in the liquid and clear of the beaker wall throughout.
Readings are rounded to whatever whole number feels close enough at the time.
Decide whether each statement is evidence for the claim, or not.
Why this matters
A conclusion can only be as sound as the data behind it. Selecting a suitable instrument, matching its resolution to the change, using a digital tool to record at even moments, and reading every value with precision is what makes results trustworthy. A careless reading or an edited record leads to wrong conclusions, no matter how well the rest of the plan was designed.
Quick self-check
1. You want to track the temperature change as a salt dissolves and the beaker warms. Which digital tool generates the most precise record?
2. Choosing the right resolution for the job means picking an instrument whose finest scale...
3. Which recorded temperature shows the highest precision?
4. Why log the temperature automatically at set intervals rather than glancing and jotting numbers by hand?
5. One logged point sits far above its neighbours. Careful practice says you should...