ACARA v9 CONTENT DESCRIPTION “examine how people use data to develop scientific explanations”
Builds on collecting data and noticing patterns. Now we follow weather scientists, who measure the rain and the temperature every day for months. They do not guess what the weather is doing. They read their records, find the pattern, and use that data to build a scientific explanation you can rely on.
Weather scientists collect data every day
A weather scientist starts every morning the same way. She reads the rain gauge to see how many millimetres of rain fell, and she reads the thermometer to see how warm it is in degrees. She writes both numbers down. One day on its own does not tell her much. But once she has weeks and months of these recorded numbers, she can lay them all out, look for the pattern, and explain what the weather is really doing.
Rainfall recorded month by month
Switch between the table, the bar chart and the line graph. The same recorded numbers tell a clear story once you draw them out.
Early in the year the rain gauge fills up, then the monthly totals drop away to almost nothing by June. The graph makes the trend leap out. This recorded data lets the scientist explain that this place has a wet season early in the year and a dry season later, rather than just guessing.
The same months, measured a different way
Rainfall is only half the story. The scientist also records the temperature each month. When she puts those numbers on a graph too, she can compare the two sets of data side by side and ask whether they move together. That comparison is the start of an explanation about how the warmth, the rain and the seasons are linked.
Temperature recorded over the same months
Look at how the temperature readings change. Then think about how this data sits next to the rainfall data above.
The temperature readings fall steadily from the hot, wet start of the year to the cool, dry middle of the year. Read alongside the rainfall data, the warmest months are also the wettest. That recorded pattern points the scientist toward an explanation: the sun heats the water, lifts it into the air, and it falls again as rain in the warm season.
Data builds the explanation, step by step
A scientist does not arrive at the full explanation in one go. She starts with very little, so her first idea is barely more than a guess. As each new piece of recorded data arrives, the explanation sharpens. Watch how the water-cycle explanation grows out of the evidence, and is never simply made up.
From recorded data to the water-cycle explanation
Add each new piece of weather data in turn and watch the scientist's explanation get sharper and better supported.
New evidence (1 of 4)
The scientist notices that some months feel rainy and others feel dry, but she has not measured anything yet.
Accepted model: Maybe the rain just comes whenever it feels like it, with no real reason.
Add the next piece of evidence and watch whether the accepted model holds or has to change.
Explanation, or just a guess?
A scientific explanation has to be built from evidence. Some of the statements below are explanations that the recorded weather data actually supports. Others are only guesses, with no data behind them. Sort each one into whether it is an explanation backed by the data, or a guess that the data does not support.
Which statements are explanations the data supports?
A real explanation is built from the recorded weather data. Decide which statements the rainfall and temperature records actually support.
Claim: The recorded data shows this place has a warm wet season early in the year and a cool dry season later on.
The rain gauge totals fell from over 200 mm in January to only 8 mm in June, so the records show the rain is drying up across these months.
The temperature readings dropped from 31 degrees to 14 degrees over the same months, matching the move from the wet season into the dry season.
The warmest, sunniest months were also the wettest, which fits the water cycle lifting water into the air and dropping it as rain.
It is going to rain next Tuesday because that is the day I would like it to rain.
The dry season must be caused by people forgetting to do a rain dance.
Decide whether each statement is evidence for the claim, or not.
Why this matters
A weather scientist reading a rain gauge, a thermometer and a year of records is doing what all scientists do: collecting honest data and using it to build an explanation. The explanation of seasons and the water cycle is not a guess that someone liked the sound of. It is built from evidence, measured day after day, that anyone can check. That is what makes a scientific explanation one you can trust.
Quick self-check
1. Weather scientists write down the rainfall and temperature every single day. Why record the numbers instead of just remembering the weather?
2. A scientist looks at a chart and sees rain fell on most days for three months, then stopped for the next three. What can she build from that data?
3. Which of these is a scientific explanation that is backed by recorded weather data?
4. The water cycle says the sun heats water, it rises as vapour, then falls again as rain. How could weather scientists support that explanation with data?
5. Why is an explanation built from recorded weather data stronger than one built from a single hunch?