IB Biology SLSkill Set 2 — Using TechnologyPaper 1 & 2Practical skill~9 min read
Using Tech to Collect Data
A person with a stopwatch and a thermometer can take a reading every thirty seconds for about ten minutes before they get bored and start guessing. A data logger takes one every second, all night, and never blinks. This page is about the three ways technology gets data for you — sensors, databases and models — and where each one can quietly mislead you.
📘 What you need to know
A sensor is an input device: it detects a change in its surroundings and turns it into an electrical signal.
A data logger stores those signals over time, then sends them to a computer to be tabled and graphed.
Loggers beat people on speed, consistency and stamina — but a badly calibrated sensor is wrong every single time.
The logging interval has to match how fast the thing you are measuring actually changes.
Databases are structured collections of data, so they can be searched, sorted, filtered and analysed quickly — DNA and protein sequences, chromosome data, population records.
Models, simulations and mesocosms generate data that informs predictions about the real thing.
Sensors and data loggers
These two words get used as if they mean the same thing. They do not. The sensor is the part that senses; the logger is the part that remembers.
A sensor responds to a specific change in its surroundings — the temperature rising, the pH dropping, more light arriving — and converts that change into an electrical signal. The data logger takes that signal at set moments and stores it. Plug the logger into a computer and the readings become a table, then a mean, then a graph, then a gradient, all of it faster and more accurately than you could manage by hand.
One logger can run several sensors at once, which is how you record temperature, pH and oxygen from the same tank in a single experiment.
Sensors you should be able to name
Sensor
What it measures
A practical it fits
Temperature probe
Temperature, often to 0.1 °C
Enzyme activity across a temperature range
pH meter
Hydrogen ion concentration, as pH
Effect of pH on enzyme activity
Oxygen sensor
Dissolved oxygen or oxygen in air
Photosynthesis rate in pondweed
Carbon dioxide sensor
Carbon dioxide concentration
Respiration of germinating seeds
Light sensor
Light intensity
Light intensity along a woodland transect
Humidity sensor
Water vapour in the air
Transpiration rate in different conditions
Heart rate monitor
Beats per minute
Recovery time after exercise
Spirometer
Lung volume and breathing rate
Ventilation during and after activity
Notice that a temperature probe does the same job as a thermometer, just better and without you standing there. If a question asks why you would use one, the answer is about frequency and consistency, not about the sensor being magic.
What a logger does better than you — and what it does not
Genuine advantages
It records far more often. Readings every second, rather than every thirty, so short-lived changes actually show up.
It catches the start. Reactions are usually fastest in the first few seconds, which is exactly when a person is still picking up the stopwatch.
It runs unattended. Overnight, over a weekend, out in a field site. Nobody has to be there.
No reading errors. No parallax, no misreading a scale, no rounding to the number you expected.
It never gets tired, so the last reading is taken with the same care as the first.
Straight into the computer, so no copying mistakes on the way into your table.
Where it can let you down
A wrongly calibrated sensor is wrong every time — consistently, invisibly, in the same direction.
It records nonsense just as happily as sense. A probe that slipped out of the water still produces a lovely smooth line.
It has no judgement. It will not notice that the pondweed died an hour ago.
Equipment costs money and needs charging, and a flat battery halfway through an overnight run means no data at all.
Choosing the logging interval
This is the decision students skip, and it is the one that decides whether your data is any use. The interval has to be short enough to capture how fast the thing you are measuring changes.
Too slow and you miss the event. Too fast and you drown in thousands of near-identical readings. Match the interval to the speed of the change.
WORKED EXAMPLE
An enzyme reaction is finished within 60 seconds. A student sets the logger to record every 30 seconds. Explain the problem and suggest a better setting.
Step 1: how many readings does that give?at 0 s, 30 s and 60 s = 3 readingsStep 2: why that is a problem
Three points cannot show the shape of a curve, and the fastest part of the reaction — the first few seconds — is missed completely.
Step 3: a better intervalevery 1 s gives 61 readings across the same 60 sLog every 1 s, so the initial rate can be measuredRule of thumb: aim for enough points that you could draw the curve without joining the dots by guesswork.
WORKED EXAMPLE
An oxygen sensor logs a rise from 2.4 mg dm−3 to 9.6 mg dm−3 over 12 minutes of photosynthesis. Calculate the mean rate.
Step 1: find the change9.6 − 2.4 = 7.2 mg dm−3Step 2: divide by the time7.2 ÷ 12 = 0.60.6 mg dm−3 min−1This is a mean rate across the whole run. For the rate at one moment you take the gradient of a tangent — which is exactly the job the computer can do for you.
Calibration: the error a logger cannot fix
A sensor gives you a number, and the number looks authoritative because it came off a screen with a decimal place. That is exactly the trap. If the sensor is out by half a degree, every reading is out by half a degree, and no amount of repeating will show it up.
You calibrate against something you already know the answer to: a pH meter in buffers of known pH, a temperature probe in melting ice at 0 °C. If the reading does not match, you adjust the sensor before you start — not afterwards.
WORKED EXAMPLE
A probe reads 0.4 °C too high on every reading. Name the type of error, say whether repeating helps, and give one fix.
Type of error
A systematic error — the same size, in the same direction, on every reading.
Does repeating help?
No. All the repeats shift by the same 0.4 °C, so the mean shifts too and the readings still look beautifully consistent.
The fix
Calibrate the probe in melting ice before the experiment, and adjust it so it reads 0.0 °C.
Systematic error — fix it by calibrating, not by repeatingRepeats expose random error. Only calibration exposes systematic error.
Databases: data somebody else already collected
A database is a structured collection of data. Structured is the key word — because everything is stored in a consistent format, it can be searched, sorted, filtered and analysed in seconds, on a scale no individual could manage.
DNA and protein sequence databases let you pull the sequence of a gene from several species and compare them base by base. More shared sequence generally means a more recent common ancestor.
Chromosome data covers numbers, sizes and gene positions across species.
Population and ecological records stretch back decades, which is how anyone can study long-term change without waiting decades.
Why this matters for your IA: a database investigation is a legitimate internal assessment. You are not collecting the data, so your skill has to show in the question you ask, the way you select and process the data, and how carefully you handle its limitations.
Models, simulations and mesocosms
Sometimes you cannot run the real experiment — it would take fifty years, or a whole lake. So you build something that behaves like it and collect data from that instead.
Mathematical models and simulations
Population growth curves are the standard example: give the model a starting population, a growth rate and a carrying capacity, and it predicts what happens next.
The model produces data, and that data informs predictions — how a fishery responds to quotas, how fast an invasive species spreads.
Change one input and run it again. That is an experiment you could never do on a real population.
Mesocosms
A mesocosm is a small enclosed ecosystem — a sealed tank or large jar with water, plants, small animals and soil. It is a real ecosystem, just a small controlled one, so you can change a variable and watch what happens without touching a real habitat.
A mesocosm is a model, so it is simpler than the real thing: fewer species, no immigration, no weather. Useful, but do not read too much into it.
Every model is a simplification — that is the point of it, and it is also its limitation. If a question asks you to evaluate a model, say what it leaves out.
💡 Exam tip
Sensor and logger are different things. The sensor detects; the logger stores.
When asked for advantages of a data logger, give frequency, consistency, running unattended and no reading errors — not just “it is more accurate”.
Mention calibration whenever a method uses a sensor. It is a quick, reliable mark.
Justify your logging interval in the IA: say how fast the change is, then set the interval to match.
For databases, use the words searched, sorted, filtered — that is what “structured” buys you.
Evaluating a model or mesocosm? Name a specific thing it leaves out.
⚠ Common mix-up
“A data logger removes all error.” It removes reading error. A miscalibrated sensor is untouched.
Thinking more readings means more accuracy. More readings improve the detail of the curve, not the correctness of each point.
Leaving the logging interval to whatever it was set to last time. That is a choice, so make it deliberately.
Confusing a mesocosm with a simulation. A mesocosm contains real living organisms; a simulation is maths on a screen.
Treating model output as measured data. A prediction is only as good as the assumptions behind it.
Using database data without checking how it was collected, then explaining a pattern that is really a change in recording method.
Up next: Using Tech to Process Data — spreadsheets, choosing the right graph, error bars, and the calculations you should let a computer do for you.
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