Repeated Chance Experiments
Chance becomes something you can do
The first probability unit talked about chance; this one rolls up its sleeves and tests it. A chance experiment is any action with an uncertain result — a coin flip, a die roll, a spin — and the new idea in Year 3 is to repeat it many times and record what happens. Doing this reveals two things at once: which outcomes are possible, and how the results vary from one run to the next. Conducting repeated experiments and tallying them connects probability to the data-handling of the Statistics strand, and it turns chance from a word into something a child can observe directly.
One experiment, an uncertain result
A single flip of a coin is the simplest chance experiment, with two possible outcomes: heads or tails. The defining feature is that you cannot know beforehand which will land — that uncertainty is exactly what makes it a chance experiment rather than a sure thing. Identifying the possible outcomes first, before any flipping, is the starting point the curriculum asks for, because every later observation is measured against that list. A coin can only ever land heads or tails, so those are the only outcomes the tally will ever record.
Repeating builds a tally
Flipping ten times is a small repeated experiment, and tallying the heads and tails turns a string of single results into counts. Because heads and tails are equally likely, ten flips often land somewhere near five and five — but rarely exactly, and sometimes quite far off. This is the heart of the unit: a single result tells you little, but repeating the experiment and counting the outcomes begins to show a pattern. The tally is the same recording tool from the Statistics strand, now applied to the results of chance.
Results vary from run to run
Run the same ten-flip experiment again and the number of heads will usually be different: six one time, four the next, five the time after. This run-to-run difference is called variation, and recognising it is a key Year 3 idea. Variation is not a mistake or a sign something went wrong — it is simply what chance does, and expecting identical results every time is the misconception this unit corrects. Two honest runs of the same experiment can and usually do disagree, and that is perfectly normal.
Many outcomes, equally likely
A die widens the experiment to six possible outcomes, all equally likely. Rolling it many times and tallying the six faces shows them coming up in roughly similar numbers — close, but never perfectly equal, because variation is always present. The more rolls, the more even the six tallies tend to look, which hints at the idea that equally likely outcomes happen about equally often in the long run. Watching the bars grow is a concrete way to see fairness play out across many trials rather than just be asserted.
When outcomes are not equal
Not every experiment has equally likely outcomes, and a spinner that is half one colour shows this plainly. Over many spins, the colour covering half the spinner comes up about twice as often as a colour covering a quarter, and the tallies reflect those unequal chances. This links back to the first unit's marble bag: the bigger the share, the more likely the outcome, and now repeated trials let a child watch that likelihood turn into actual counts. Unequal outcomes produce unequal tallies, in proportion to their chances.
More repeats, steadier share
A final, powerful observation: the more times an experiment is repeated, the more settled the share of each outcome becomes. With only a handful of flips the fraction of heads can swing wildly — all heads, then none — but across dozens or hundreds it drifts closer to about half and stays there. Young children meet this only informally, as a felt pattern rather than a rule, but it is the seed of one of the deepest ideas in probability. With single experiments, tallies, variation, equal and unequal outcomes, and the steadying effect of many trials all explored, a child has conducted real repeated chance experiments and seen what they reveal — completing the Year 3 Probability strand.