ACARA v9 CONTENT DESCRIPTION “examine how people use data to develop scientific explanations”
Builds on watching the world closely and noticing what happens. Here we take the next step: people do not just look, they collect data. Data means the facts you gather, like numbers you measure or things you count, and people use that data to explain why something happens.
What is data, and why collect it?
When a gardener wants to know how a bean plant grows, she does not just glance at it. Each day she measures how tall it is and writes the number down. Those written-down numbers are her data. A doctor does the same when she measures how warm you are with a thermometer. Data is information people gather on purpose, so that later they can look at it all together and work out an explanation.
A gardener measures a plant each day
Switch between the table, the bar chart and the line graph. The same numbers tell a story once you draw them.
Each day the bean plant is taller than the day before. The numbers climb steadily, and the graph makes the pattern jump out. This data lets the gardener explain that her plant is growing well, a little more every single day.
Data builds a better explanation over time
People do not always get the explanation right on the first try. As they gather more data, their explanation gets better. Imagine a class trying to work out why one of their two bean plants grew much taller than the other. At first they only have a little information, so their explanation is just a guess. As they collect more data, a clearer reason appears.
More data, a better explanation
Add each new piece of data in turn and watch the class explanation get sharper.
New evidence (1 of 4)
The class notices one bean plant is much taller than the other. They have not measured anything yet.
Accepted model: Maybe the taller plant is just an older or luckier plant.
Add the next piece of evidence and watch whether the accepted model holds or has to change.
Which data really supports the explanation?
Not every fact you notice helps explain something. A good scientist keeps the data that actually backs the explanation and sets aside the facts that only sound related. The class decided that the plant with more water grew taller. Sort the notes below into the data that is real evidence for that explanation, and the data that does not test it.
Sort the data: what supports the explanation?
The explanation: the plant given more water grew taller. Decide which pieces of data actually support it.
Claim: The bean plant that was given more water grew taller than the one given less water.
The well-watered plant measured 18 cm, while the plant given little water measured only 6 cm.
The watering chart shows the tall plant was watered every day and the short plant only twice.
When both plants were later given the same water, they grew at about the same speed.
The pot holding the taller plant was a bright red colour.
The class did their measuring on a Friday morning before lunch.
Decide whether each statement is evidence for the claim, or not.
Why this matters
A doctor reading your temperature, a gardener measuring a plant, and a class testing their bean seeds are all doing the same thing: collecting data and using it to explain what is happening. Learning to gather honest data, look for the pattern, and keep only the evidence that fits is how everyone, from a young scientist to a grown-up expert, builds an explanation you can trust.
Quick self-check
1. What does the word data mean in science?
2. A gardener writes down the height of a bean plant every day. Why write it down instead of just remembering?
3. A nurse measures a sick child and gets 39 degrees, well above the usual 37. What does this single piece of data help her do?
4. A class watered one plant a lot and another plant only a little, then measured both. They are using data to...
5. Why is good data better than just a strong feeling about what happened?