4.1 Making Predictions from Evidence

4.1 Making Predictions from Evidence

The Predicting & Concluding skill area is about looking forward and wrapping up: given what you have observed or measured, what do you expect to happen next, and what are you allowed to claim at the end? This section handles the first half — predictions. ICAS (International Competitions and Assessments for Schools) asks you to make them from graphs, tables and everyday patterns, and it rewards students whose predictions are anchored in evidence rather than wishful thinking.

A Prediction Is Not a Guess

A guess is an answer pulled from thin air — "I think it will rain tomorrow because I feel like it." A scientific prediction is a statement about what you expect to happen, built from evidence you already have: measurements, a trend on a graph, or a pattern you have seen repeat. Two things make it scientific:

• It names a reason or pattern. "The plant grew about 3 cm each week, so I predict it will be about 3 cm taller next week."
• It can be checked. Someone could actually wait, measure, and say whether the prediction was right.

A prediction does not have to turn out correct to be a good prediction. Scientists make wrong predictions all the time — the point is that the prediction follows logically from the evidence and can be tested.

The If-Then Statement

Scientists often write predictions as if-then statements, which glue a cause to an expected effect:

• If the water is warmer, then the sugar will dissolve faster.
• If a plant receives more sunlight, then it will grow taller.

The "if" part is the thing you change (the independent variable) and the "then" part is what you expect to measure (the dependent variable). Writing a prediction this way forces you to say exactly what you expect, instead of a vague "it will be different."

What Makes a Prediction Testable?

A prediction is testable when you could check it with a real measurement or observation using equipment you actually have. Compare these:

On ICAS, a question may ask which of four statements is a prediction. The correct option is the one that is specific, measurable and based on the pattern given — not an opinion, a hope or a wild stab in the dark.

Extending a Trend to Predict the Next Value

The most common predicting task on ICAS is trend extension: a table or graph shows a pattern, and you must say what value comes next. The safe method is to work out the size of each step before you extend anything.

Worked example — a seedling's growth. A Year 5 class measured a bean seedling each week:

Step 1 — find the weekly increases: +3, +4, +5, +6. Step 2 — notice the pattern: each week the plant grows 1 cm more than the week before. Step 3 — extend the pattern: the next increase should be +7, so the predicted height at Week 6 is 21 + 7 = 28 cm.

Notice the prediction comes from the pattern in the numbers, not from a feeling. That is what makes it scientific.

Worked example — cooling water. Hot water cooling in a kitchen read 70, 60, 52, 46, 42 °C at two-minute intervals. The drops are 10, 8, 6, 4 — shrinking by 2 each time. The next drop should be about 2, predicting roughly 40 °C. Cooling curves always flatten out, so a prediction of 32 °C (continuing the first big drop at full size) would be wrong.

Predictions from Patterns in Nature

Some of the safest predictions come from cycles — patterns in nature that repeat:

• Day and night — the Sun rises in the east and sets in the west every day, so you can predict where it will appear in the sky tomorrow morning.
• Seasons — in Australia, December to February is summer, so you can predict that January days in Perth will be long and hot while July days will be shorter and cooler.
• Life cycles — egg, caterpillar, chrysalis, butterfly. If you spot a caterpillar munching a leaf, you can predict its next stage.

Because these cycles have repeated reliably for all of recorded history, predicting the next stage is about as safe as a prediction gets.

Two Traps ICAS Sets

1. Predicting beyond what the data supports. A pattern measured over five weeks might not continue forever. If a seedling grows steadily for five weeks, predicting Week 6 is reasonable; predicting it will be a metre tall by Christmas is not — growth slows as plants mature. Extending a short way past the data is fair; extending a long way turns your prediction back into a guess.
2. Ignoring anomalies. An anomaly is a result that does not fit the pattern — say a height of 21 cm when the trend suggested 14 cm. Do not quietly pretend it is part of the trend, and do not let one odd point hijack your prediction either. Notice it, set it aside, and base your prediction on the clear pattern in the rest of the data.