Planning & Analysing Whole Investigations

Revise Planning & Analysing Whole Investigations for Biology 6 (IAL) WBI16 (A2 Level) — revision notes, 4 practice questions and instant AI marking.

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Edexcel IAL Biology • Unit 6: Practical Skills in Biology II

Planning & Analysing Whole Investigations

🗺️ Big idea: A whole investigation is planned backwards. Decide what graph and what statistical test will answer the question, and that tells you what data you need — how many groups, how many readings, paired or unpaired. Plan forwards and you often finish with data no test can handle.

Summary — What This Topic Covers

Planning from the analysis backwards
Choosing the test before collecting the data
Deciding how many replicates, and why sample size matters
Pilot studies and what they are for
Writing a conclusion the data can actually support

1. Plan Backwards from the Analysis

ASK → WHAT TEST → WHAT DATA YOU MUST COLLECT ───────────────────────────────────────────────────────────────────── Do two groups t-test two sets of measurements, continuous, differ? roughly normal, ideally n ≥ 10 each Do observed chi-squared COUNTS in categories, not means or counts match percentages; expected values from a expected? stated hypothesis Are two Spearman's rank pairs of measurements from the same variables individuals or sites, at least 7 pairs related?

The rule that saves whole investigations

Chi-squared needs raw counts. If you record percentages or means, the test cannot be done and the data cannot be rescued afterwards. Decide the test first.

The commonest planning failure

Collecting one measurement per condition. Any test needs a spread to work with — a single value has no variability, so no test can tell signal from noise.

2. Sample Size and Replicates

More replicates narrow the spread of the mean, making a real difference easier to detect
Around 10 per group is a reasonable target for a t-test at this level; three is usually too few
In the field, more quadrats reduce the effect of patchiness
Sample size must be balanced against time — say so, and justify the number you chose

Why it matters statistically

A small sample gives a large standard error, so the confidence interval is wide and even a real difference may not reach significance. Increasing n is the most reliable way to strengthen a conclusion.

3. Pilot Studies

Key Term

A pilot study is a small preliminary run used to refine the method before the real investigation begins.

Establishes a sensible range for the independent variable
Checks the timing — whether readings can be taken fast enough, or the reaction is over too quickly
Confirms the concentration or dilution gives a measurable response
Reveals practical problems while they are still cheap to fix

Easy marks

Almost any choice in a plan can be justified with "a pilot study showed…". It is one sentence and it converts an arbitrary decision into a reasoned one.

4. Randomisation and Bias

Key Term

Bias is any systematic tendency in how samples are selected or measured that makes them unrepresentative.

In the field, place quadrats using random coordinates, not by eye — "choosing a representative spot" is exactly how bias enters
Assign organisms to groups at random, so that any pre-existing difference is spread evenly
blind
transect

5. A Conclusion the Data Supports

Practice Questions

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