Data Collection, Organisation, and Informed Decision Making

By the end of this lesson, learners should be able to

1. Collect and classify discrete and continuous primary data from classroom peers.
2. Organise raw statistical data into frequency tables for practical decision making.
3. Analyse frequency distributions using benchmark cut-offs to make informed administrative decisions.

Key vocabulary

Discrete Data

Data that can only take distinct separate values, such as count totals or specific sizes.

Shoe sizes and sibling counts collected in class represent discrete data for store inventory planning.

Informed Decision

A choice based on clear organised data analysis rather than random guessing or intuition.

Comparing shoe size frequencies helps the school store make an informed decision when ordering inventory.

Pass Mark

A predetermined minimum score required to achieve a passing standard in an evaluation.

Setting 50 marks as the pass mark allows teachers to count successful learners from data.

Threshold

A specified numerical limit used to trigger a specific decision or administrative action.

The 70 percent pass threshold determines whether the administration approves the class field trip.

Lesson preview

Slide 1 · Collecting and Classifying Peer Data for Resource PlanningModel · 18 min

Data collection involves gathering numerical or categorical information from targeted groups like classroom peers. We measure continuous attributes like height and mass, or record discrete data such as number of siblings (obtained by counting) and shoe sizes (which take only separate values). Systematic recording in tables gives context. Organising raw data into frequency tables enables decision makers to identify trends, allocate physical resources efficiently, and solve operational problems in institutions. Use Figure 1 to see how raw data is systematically recorded in a frequency table.

• Data collection from peers provides authentic primary numerical and categorical information for practical classroom analysis.
• Discrete data can take only separate distinct values, often obtained by counting, e.g. number of siblings; shoe sizes are also discrete.
• Continuous data like mass and height can assume any value within a given measurement range.
• Organising raw data into frequency tables transforms individual measurements into actionable evidence for planning.

Worked example. Count frequencies: Size 6 appears 6 times, Size 7 appears 9 times, and Size 8 appears 5 times. Total count is $6+9+5=20$. Construct frequency table with tallies: Size 6 (卌 |, frequency 6), Size 7 (卌 ||||, frequency 9), Size 8 (卌, frequency 5). Identify modal size: Size 7 has the maximum frequency of 9 learners, which is $\left(\frac{9}{20}\right)\times 100\mathrm{\%}=45\mathrm{\%}$ of the total demand. Answer: Size 7 has the highest demand with 9 out of 20 learners (45%), so the club should purchase size 7 shoes in the largest quantity..

Check question: A teacher collected assignment marks for $40$ learners: $12$ scored below the pass mark, and $28$ scored at or above the pass mark. The school mandates extra tuition if more than $25\mathrm{\%}$ of the learners score below the pass mark. Calculate the percentage of learners scoring below the pass mark and state whether extra tuition is mandated.

1. $30\mathrm{\%}$; extra tuition is mandated.
2. $30\mathrm{\%}$; extra tuition is not mandated.
3. $70\mathrm{\%}$; extra tuition is mandated.
4. $12\mathrm{\%}$; extra tuition is not mandated.

Show answer

$30\mathrm{\%}$; extra tuition is mandated. To find the percentage of learners scoring below the pass mark, divide the number of learners below the pass mark by the total number of learners and multiply by $100\mathrm{\%}$: $\left(\frac{12}{40}\right)\times 100\mathrm{\%}=30\mathrm{\%}$. Since $30\mathrm{\%}>25\mathrm{\%}$, the threshold is exceeded and extra tuition is mandated.

Slide 2 · Evaluating Performance Benchmarks for Administrative DecisionsPractice · 22 min

Teach the full lesson on the projector

This lesson, and every other Grade 1–10 CBE lesson, comes ready to project with the EAP ClassKit. It runs without electricity or internet.

• Both slides with diagrams and class activities
• Teacher notes, SNE adaptations and assessment checks
• Homework and the other 15 lessons in this sub-strand

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All 16 lessons in Statistics I

1. 1Gathering Data from Practical SituationsCollect data from real-life sources
2. 2Frequency Distribution Tables for Ungrouped and Grouped DataDraw a frequency distribution table for grouped and ungrouped data
3. 3Drawing Grouped Frequency Distribution TablesDraw a frequency distribution table for grouped and ungrouped data
4. 4Constructing Frequency Distribution Tables for Grouped and Ungrouped DataDraw a frequency distribution table for grouped and ungrouped data
5. 5Calculating Averages and Middle Values of Raw DataDetermine mean, mode and median of grouped and ungrouped data
6. 6Determining Median of Data and Introduction to Grouped Data MeanDetermine mean, mode and median of grouped and ungrouped data
7. 7Determining Mean, Modal Class, and Median of Grouped DataDetermine mean, mode and median of grouped and ungrouped data
8. 8Determining Mean, Mode, and Median of Grouped and Ungrouped DataDetermine mean, mode and median of grouped and ungrouped data
9. 9Representing Data Using Histograms and Frequency PolygonsRepresent data using histograms and frequency polygons
10. 10Constructing Histograms and Frequency PolygonsRepresent data using histograms and frequency polygons
11. 11Advanced Data Representation: Histograms and Frequency PolygonsRepresent data using histograms and frequency polygons
12. 12Interpreting Data from Histograms and Frequency PolygonsInterpret data from histograms and frequency polygons
13. 13Interpreting Histograms and Frequency PolygonsInterpret data from histograms and frequency polygons
14. 14Interpreting Histograms and Frequency PolygonsInterpret data from histograms and frequency polygons
15. 15Data Collection, Organisation, and Informed Decision MakingPromote data collection, organisation and representation for informed decision making
16. 16Data Interpretation and Decision MakingPromote data collection, organisation and representation for informed decision making