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Doing Your Project and Analysing the Results

After planning, you carry out the project in a loop of plan, do and check, and you keep a dated log. You record data at once, check odd values, then analyse with mean, range and charts. Finally you compare the result with your hypothesis, say honestly what the limits were, and write a conclusion.

🎬 Step-by-step story

  1. A project is a loop: Plan, Do, Check, then adjust the plan. Write every step in a dated project log.
  2. Collect data. Each blue bar is one measurement, here the height of one plant. Write it down at once with its unit.
  3. Check the data. One red bar is far away from the rest. Look for the reason before you decide what to do with it.
  4. Analyse. The mean is the sum divided by how many. See how one odd value pulls the gold line up above the green line.
  5. Conclude. Put the result beside your hypothesis. Does the data support it? Then name the limits and the next step.
  6. Free play: use the slider to take more measurements and keep or remove the odd value. Watch how the mean changes.

Tip: drag the 3D scene to turn it. Use two fingers to zoom.

🤔 Common doubts, cleared

Why do I need a project log?

It shows what you did and when, helps you remember reasons for changes and proves the work is yours. The loop in the 3D shows where the log is used: at every round.

Why repeat the measurement many times?

One reading can be wrong. With many bars you can see what is typical. Compare the eight blue bars in the 3D.

What do I do with the odd value?

Find the reason, record it, and be open about whether you keep it or remove it. Do not hide it.

Why does one odd value change the mean so much?

The mean uses every value, so one huge number pulls it up. The gold line is higher than the green line only because of the single red bar.

What if the result does not match my hypothesis?

You still report it. Say what the data shows, give possible reasons and limits. That is a real finding.

How many measurements are enough?

More is steadier. Move the slider from 1 to 9 and see the mean settle. Ask your teacher what is reasonable for your project.

Carrying out the project: plan, do, check

Take your plan from the earlier stage and work in short rounds.

  1. Plan the next small piece (for example "measure 8 plants on Monday").
  2. Do it in the same way every time.
  3. Check: did it work? Is the data sensible? If not, change the plan.

Keep a project log: date, what you did, what you saw, what went wrong, what you changed. Keep your time plan with small deadlines, keep your tools safe, and ask a teacher or expert when you are stuck. Keep copies of your files.

Collecting and recording data

Write results at once, with the unit, the date and how you measured. Make a table with a clear heading for each column. Repeat each measurement several times, because one measurement can be wrong. For a survey, ask the same questions in the same words to enough people, and keep names private. For a product, model, event or artwork, record tests and feedback: who tried it, what happened, what they said.

Checking the data honestly

An outlier is a value far from the others. It may be a typing mistake, a measuring error, or a real surprise. Find out which. Write it in the log. If it is a proved mistake you may leave it out, but you must say so in the report. Never remove values just because they do not match your hypothesis. Honest results are worth more than nice ones.

Analysing: mean, median, range, percent and charts

Choose a chart that fits: a bar chart to compare groups, a line graph for change over time, a pie chart for shares of a whole. Label axes with names and units, give a title.

Drawing conclusions and naming limits

Put your result next to the hypothesis or goal. Say: "The data supports / does not support the idea because ... (give numbers)". A result that does not support your idea is still a good result. Then write the limits (small sample, one place, short time, tool error) and the next step (more trials, a better tool, another group). Do not claim more than the data shows: "plants with more light grew taller in our test" is better than "light always makes plants taller".

Try it

Mini-experiment (10 minutes). Drop a ball from the same height 8 times and time the fall with a phone stopwatch. Write each time in a table. Spot any odd value, find the mean and the range, and make a bar chart. In the 3D above (step 5), move the slider to see how more measurements steady the mean. Write one sentence of conclusion and one limit.

Key formulas and definitions

Worked examples

1. Find the mean of the plant heights 12, 14, 13, 15 cm.

Sum = 12 + 14 + 13 + 15 = 54. Number = 4. Mean = 54 ÷ 4 = 13.5 cm.

2. Find the median of 3, 9, 4, 8, 5.

Order: 3, 4, 5, 8, 9. The middle (third) value is 5. Median = 5.

3. 18 of the 24 students surveyed liked the new timetable. What percent is that?

18 ÷ 24 × 100 = 75%.

4. The eight values 12, 14, 13, 15, 13, 14, 13, 15 have a ninth value 40 added. What happens to the mean?

Before: 109 ÷ 8 = 13.6. After: 149 ÷ 9 = 16.6 (about). The single odd value raised the mean by 3 cm. That is why we check the data before we calculate.

5. Plants in the dark average 13.6 cm; plants in light average 17.0 cm. Find the percent change.

(17.0 − 13.6) ÷ 13.6 × 100 = 3.4 ÷ 13.6 × 100 = 25%.

6. Your data does not support your hypothesis. What should you write in the conclusion?

Say clearly that the data does not support the hypothesis, give the numbers, and suggest reasons and limits (sample size, conditions). Do not change the data. A clear "no" with evidence is a good result.

Common mistakes

Practice quiz

1. What do you write in the project log?
2. The mean of 2, 4, 6 is:
3. An outlier is:
4. Range of 5, 9, 12 is:
5. If the data does not support your hypothesis you should:

Practice: answer these yourself

Type or choose your answer, then press Check. Use a hint if you are stuck; the full solution appears after you answer.

Frequently asked questions

How do I analyse the results of my project?

Put data in a table, check for odd values, find the mean, median and range, draw a suitable chart, and compare the result with your hypothesis or goal.

What if my results are not what I expected?

That is fine. Report them honestly, explain possible reasons, name the limits and say what you would try next. Honest results are valued more than perfect ones.

What if my project is a product or an event, not an experiment?

Test it with real users, record their feedback and results in numbers or short notes, compare with your goal, and write what worked, what did not and what you would improve.

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