Conducting Investigation II

Overview

Students complete data collection for their class statistical investigations, focusing on discrete and continuous numerical variables and preparing data for analysis. This lesson builds directly toward interpreting distributions using summary statistics and shape.

Learning intentions

• Students will complete data collection using a planned procedure for their investigation.
• Students will organise collected data for discrete and continuous numerical variables in a systematic way.
• Students will check data quality by spotting missing values, inconsistent units, and obvious errors.
• Students will prepare a clean dataset for the next lesson’s analysis.

Success criteria

• I can record data systematically in a table using consistent units and headings.
• I can organise discrete and continuous numerical data so it is ready for summary statistics.
• I can identify and correct common data issues (missing entries, unit mistakes, duplicate rows).
• I can explain what my data represents in context.

Curriculum links

• AC9M7ST01: acquire data sets for discrete and continuous numerical variables; calculate range, median, mean and mode; justify which measures suit the distribution.
• AC9M7ST03: plan and conduct statistical investigations involving numerical variables; analyse and interpret distributions; report findings in terms of shape and summary statistics.
• AC9M7ST02: create data displays (including stem-and-leaf plots using software where appropriate); describe distribution using shape, centre and spread, including outliers.

Lesson structure (60 minutes)

1. 0–5 min · Opening chat & revisit goal. Teacher briefly asks groups: “What variable are you measuring, and is it discrete or continuous?” Students share answers and clarify in their investigation notes.

Success criteria check: students restate variable type and units.

2. 5–15 min · Procedure & quality quick-check. Teacher models a fast “data-collection checklist” on the board (headings, units, one row per trial/participant, decimal places where needed, consistent recording rules). Students complete a checklist for their own plan and highlight what they must do today.

WALT reminder: complete data collection and organise it systematically.

Success criteria check: students confirm table headings and units before collecting.

3. 15–35 min · Data collection rounds. Teacher runs stations or class rounds so students can collect remaining data (e.g., reaction-style timing, measurements like height/arm span, counts like number of items). Students record data directly into their prepared organiser (paper table or digital sheet).

Ongoing teacher action: circulate to spot unit errors, missing values, and duplicates; coach students to correct immediately.

Success criteria check: teacher confirms most groups are recording consistently.

4. 35–45 min · Clean-up and organise dataset. Teacher gives a short “sorting moment”: teams scan their dataset for issues—blank cells, swapped columns, impossible values, inconsistent units/rounding. Students fix problems and ensure each data row matches the intended variable meaning.

Success criteria check: students label any uncertain entries and decide whether to re-measure or mark as missing according to class rules.

5. 45–55 min · Mini-data display prep (ready for next lesson). Teacher demonstrates how their clean dataset will be turned into a display later (dot plot or stem-and-leaf). Students prepare the next-step layout: they ensure their data list is in order (or grouped into tens for continuous measures) and that the dataset can be copied into software.

Success criteria check: students can point to their ordered dataset and explain how they’ll use it.

6. 55–60 min · Exit ticket: data readiness. Students complete a short exit ticket: (1) variable type (discrete/continuous), (2) units used, (3) one data quality check they completed, (4) “My dataset is ready because…”. Teacher collects for quick feedback.

Resources

• Investigation plan sheets and variable definitions from previous lessons
• Data recording templates (tables with clear headings and units)
• Clipboards or printed data sheets for recording
• Digital device access (optional): Excel/Google Sheets or classroom spreadsheet
• Rulers, measuring tapes, stopwatches/timers, counters/spinners (as relevant to each group’s investigation)
• Coloured pens/highlighters for checking headings and units
• Teacher checklist for data-quality checks
• Exit ticket slips

Assessment

• Formative: observation during data collection (accuracy of headings, units, and consistent recording)
• Formative: teacher feedback during the clean-up scan (identifying missing/incorrect entries)
• Summative (informal): exit ticket confirming dataset readiness and variable/unit correctness

Differentiation

• Support for diverse learners: provide a partially completed table template (headings and unit prompts) and a word bank (e.g., “record in cm”, “one row per trial/participant”).
• Support for dyslexia-friendly needs: use templates with larger font, high-contrast paper, and consistent layout; provide sentence starters for exit ticket responses.
• Technology option: students who benefit from scaffolding can enter data directly into Excel/Sheets with column formatting set up in advance (numeric format, units in titles, automatic totals).
• Challenge/extension (within lesson aim): students identify potential outliers (even before analysis) by applying a “reasonable value” rule from their context and noting any actions taken.

Dyslexia-friendly reading options

• Provide a “read-aloud script” for the data-collection checklist (teacher reads; students follow with their own copy).
• Offer short, colour-coded cards: “Headers”, “Units”, “One entry per trial”, “Check decimals”.
• Allow students to use audio recording (teacher-approved) to re-state the variable and units for their exit ticket instead of writing full sentences.