Statistical Enquiry Cycle: E Evaluation and review — Edexcel A-Level Statistics
Test yourself on Statistical Enquiry Cycle: E Evaluation and review with PEARSON EDEXCEL A-Level practice questions.
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Statistical Enquiry Cycle: E Evaluation and review explained
Evaluation and review requires the student to scrutinise how data were collected and how they were displayed, and to name specific weaknesses rather than offer general doubt.
Read the full explanation
For collection, weaknesses include a sampling frame that omits part of the population, non-random or self-selecting samples, leading or ambiguous question wording, low response rates producing non-response bias, unclear definitions of variables, and measurement or recording error. For display, weaknesses include axes that do not start at zero or are truncated without a break, unequal class intervals drawn as if equal, misleading area or volume scaling, missing units or labels, overplotting that hides structure, and charts chosen for decoration rather than for the data type. A useful method is to ask of each stage: who or what was selected, how, what was measured, and how the result was shown.
Your focus
- Identify specific weaknesses in the approach used to collect a given set of data.
- Identify specific weaknesses in the approach used to display a given set of data.
- Explain how an identified weakness could affect the conclusions drawn from the data.
Statistical Enquiry Cycle: E Evaluation and review exam tips
Marking Points
- Identifies a specific weakness in how data were collected, such as a biased sampling method, a defective sampling frame or non-response.
- Identifies a specific weakness in how data were displayed, such as a misleading scale, inappropriate chart type or missing labels and units.
- Links each weakness to its likely effect on the conclusions drawn, rather than merely naming it.
- Distinguishes weaknesses in collection from weaknesses in display when both are present.
- Uses correct statistical vocabulary, for example bias, sampling frame, class interval, axis scale and outlier.
- Suggests an appropriate improvement that addresses the identified weakness.
Examiner Tips
- 💡Read the scenario and list the collection stage and the display stage separately before evaluating either.
- 💡Name the weakness precisely, then state the direction of its likely effect on the conclusion.
- 💡Prefer options that identify a mechanism over options that express vague distrust of the data.
- 💡Check display options for scale, labelling, class intervals and chart type, since these are common sources of distortion.
Common Mistakes
- Stating that the data are simply wrong or unreliable without naming the specific collection or display feature responsible; correction: identify the mechanism, such as self-selection or a truncated axis, and its effect.
- Confusing a weakness in collection with a weakness in display; correction: separate the two stages and attribute each criticism to the correct one.
- Assuming a large sample removes bias; correction: a large biased sample remains biased, so size does not cure a flawed selection method.
- Treating an unusual value as automatically an error; correction: an outlier may be genuine, so investigate recording and measurement before labelling it a mistake.