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PsychologyPsychology Experimental StudySL

Does chunking improve the recall of digit strings?

A well-grounded replication with a clear hypothesis and an appropriate design, let down by a descriptive statistic reported without a measure of spread and an analysis that doesn't fully link the result back to the background theory.

~2050 words · About 1,800–2,200 words. Tables, graphs, references and appendices are not counted.

Estimated

14–18

out of 22

Good
AI estimate — not an official IB grade.

Draft history

How this commentary developed, draft by draft — a record you can show your teacher.

Draft 1

20 Aug 2026

11/22
A 3/6B 3/4C 2/6D 3/6

~1850 words

3/3 checklist done

13/22+2
A 3→4/6B 3/4C 2→3/6D 3/6

~2050 words

0/3 checklist done

Focus on these first

The highest-impact changes, in order.

1

Report spread, not just the mean

Add standard deviations and error bars, and justify your choice of statistics.

Analysis (Criterion C)

2

Tighten the theory-to-hypothesis link

Make the step from Miller's theory to your exact operationalised prediction explicit.

Introduction (Criterion A)

3

Target your evaluation

Match each improvement to the limitation it fixes and discuss generalisability.

Evaluation (Criterion D)

Comments on your text

Working wellProblemSuggestion

Click a highlighted passage to see its comment.

This study investigates whether grouping digits into chunks improves short-term recall, based on Miller (1956)1. Participants were randomly allocated to either the chunked or the unchunked condition, and the digit strings were the same length in both. The mean number of digits recalled was higher in the chunked condition (8.4) than the unchunked condition (6.1)2. The results support Miller's idea that chunking increases the capacity of short-term memory3. A limitation is that the sample was drawn only from the researcher's own year group, which limits generalisability.

Criterion breakdown

Criterion A

Introduction

4 / 6

DecentMedium confidence

How to improve

  • Make the step from Miller's theory to your exact hypothesis explicit
  • State the operationalised variables in the introduction

To reach 5/6

Draw the line from the background theory directly to your operationalised hypothesis so the aim follows logically.

Criterion B

Exploration

3 / 4

GoodMedium confidence

How to improve

  • Justify the sampling method and acknowledge its bias
  • State how extraneous variables were controlled

To reach 4/4

Justify your sampling and controls so the design choices are clearly reasoned, not just stated.

Criterion C

Analysis

3 / 6

DecentMedium confidence

How to improve

  • Report a measure of spread (standard deviation) and add error bars
  • Justify your choice of statistics

To reach 4/6

Report dispersion with the mean, add error bars, and justify the descriptive (or inferential) statistics you chose.

Statistics and graph

Image 1 · Bar chart of group means

Figure 1 — Mean digits recalled by condition

Analysis section

Needs improvement
  • Add error bars (± one standard deviation)
  • Interpret the size of the difference in terms of chunking

Criterion D

Evaluation

3 / 6

DecentMedium confidence

How to improve

  • Tie each improvement to the limitation it addresses
  • Discuss generalisability of the sample

To reach 4/6

Link findings to theory, and give targeted improvements that each address a specific limitation.

What's already working

  • A theory-led, testable hypothesis
  • An appropriate design with random allocation
  • A clear comparison of conditions
  • Findings connected back to the background study

Revision Checklist

0 / 3 done