WJEC Year 10 Statistics: Experimental & Practical Assessment Essentials | WJEC 十年级统计：实验与实践考核要点

📚 WJEC Year 10 Statistics: Experimental & Practical Assessment Essentials | WJEC 十年级统计：实验与实践考核要点

In WJEC Year 10 Statistics, the experimental or practical assessment is designed to test your ability to carry out a full statistical enquiry cycle. It is not just about calculating numbers – you need to demonstrate that you can plan an investigation, collect data sensibly, use appropriate diagrams and summary measures, and communicate your findings clearly. This article covers the essential skills and knowledge you must show in your practical work, from formulating a hypothesis to evaluating the reliability of your conclusions.

在 WJEC 十年级统计课程中，实验或实践考核旨在检验你执行完整统计探究周期的能力。它不仅仅是计算数字——你需要展示你能够合理规划调查、明智地收集数据、使用恰当的图表和汇总指标，并清晰地传达你的发现。本文涵盖了实践作业中必须展示的关键技能和知识，从提出假设到评估结论的可靠性。

1. Understanding the Practical Assessment Objectives | 理解实践考核目标

The practical assessment follows the statistical enquiry cycle: pose a question, plan data collection, gather data, process and present data, interpret results, and evaluate the process. You are marked on your ability to apply statistical techniques in a real‑world context rather than just reproducing textbook methods. Examiners look for evidence of independent thinking, logical planning, and an awareness of limitations.

实践考核遵循统计探究循环：提出问题、规划数据收集、收集数据、处理和呈现数据、解释结果以及评估过程。评分依据是你将统计技术应用于真实世界情境的能力，而不仅仅是复现课本方法。考官会寻找独立思考、逻辑规划和对局限性的意识的证据。

Your work will be assessed against criteria such as experimental design, data accuracy, appropriate choice of diagrams, correct calculations, valid conclusions, and critical evaluation. Always review the specification’s mark scheme so you know exactly what is expected for each band of marks.

你的作业将根据实验设计、数据准确性、图表的恰当选择、正确的计算、有效的结论和批判性评估等标准来评分。一定要查看课程大纲的评分方案，这样你就能确切地知道各分数段的要求。

2. Formulating Clear Questions and Hypotheses | 提出清晰的问题与假设

Every practical investigation begins with a statistical question that can be answered with data. Avoid vague questions like ‘Are students fit?’ Instead, formulate a precise question such as ‘Is there a difference between the resting heart rates of Year 10 boys and girls?’ A well‑defined question drives your entire enquiry.

每一项实践调查都始于一个能用数据回答的统计问题。避免模糊的问题，如“学生健康吗？”。相反，要提出一个精确的问题，例如“十年级男生和女生的静息心率是否存在差异？”。一个定义明确的问题会驱动你的整个探究。

State a null hypothesis and an alternative hypothesis if you are carrying out a significance test. For example, ‘H₀: There is no difference in mean reaction times before and after exercise,’ and ‘H₁: There is a difference.’ Even if formal testing is not required, a prediction helps focus your data collection.

如果进行显著性检验，要陈述原假设和备择假设。例如，“H₀：运动前后的平均反应时间没有差异”，以及“H₁：存在差异”。即使不要求正式检验，一个预测也有助于聚焦数据收集。

3. Designing the Investigation with Control and Randomisation | 通过控制和随机化设计调查

A well‑designed experiment or survey reduces bias and increases the reliability of your conclusions. Identify the independent variable (the one you manipulate or compare groups by) and the dependent variable (the one you measure). List all variables that must be kept constant to ensure a fair test, such as time of day, equipment used, or environmental conditions.

一个设计良好的实验或调查能减少偏差并提高结论的可靠性。识别自变量（你操纵或用来分组的变量）和因变量（你测量的变量）。列出所有必须保持恒定的变量以确保公平测试，如时间、所用设备或环境条件。

Use randomisation where possible: randomly allocate participants to treatment groups, randomise the order of trials, or use random numbers to select sampling units. This minimises the impact of confounding variables and experimenter bias. Pilot your procedure on a small scale to check for practical problems before committing to full data collection.

尽可能使用随机化：将参与者随机分配到处理组、随机化试验顺序，或使用随机数选择抽样单位。这可以最小化混淆变量和实验者偏差的影响。在全面收集数据之前，先小规模试行你的程序，以检查实际问题。

4. Sampling Techniques in Practice | 实践中的抽样技术

Selecting an appropriate sample is crucial. Be prepared to explain and justify your choice of sampling method. The main methods you may use or discuss are:

选择合适的样本至关重要。准备好解释并证明你选择的抽样方法是合理的。你可能会使用或讨论的主要方法有：

Simple random sampling – every member of the population has an equal chance of selection. Use random number tables or a calculator’s random integer function.

简单随机抽样——总体中的每个成员被选中的几率相等。使用随机数表或计算器的随机整数功能。
Stratified sampling – population divided into distinct groups (strata) and random samples taken from each in proportion to size. This guarantees representation of key subgroups.

分层抽样——将总体分为不同的组（层），并按比例从每层中抽取随机样本。这保证了关键子群体的代表性。
Systematic sampling – choosing every nth individual from a list. Quick but can introduce periodicity bias.

系统抽样——从列表中每隔n个个体抽取一个。快速，但可能引入周期性偏差。
Cluster sampling – splitting the population into clusters and randomly selecting whole clusters. Useful when travel is an issue.

整群抽样——将总体分成群，并随机抽取完整的群。当交通不便时有用。
Opportunity (convenience) sampling – using people who are easily available. Quick but likely to be biased; only acceptable if properly acknowledged in evaluation.

机会（便利）抽样——使用容易接触到的人。快速，但可能产生偏差；只有在评估中恰当说明时才可接受。

Always state the target population and sampling frame you used. Mention any practical constraints that affected your choice, such as time, access, or safety.

始终说明你使用的目标总体和抽样框。提及任何影响你选择的实际限制，如时间、可及性或安全因素。

5. Data Collection Tools and Accuracy | 数据收集工具与准确性

Design your data collection sheet or questionnaire carefully. It must be clear, unambiguous, and capable of recording all necessary information. Use tally charts, frequency tables, or pre‑formatted tables with columns for each variable. Pilot your form to spot ambiguous wording or missing categories.

仔细设计你的数据收集表或问卷。它必须清晰、无歧义，并能记录所有必要信息。使用计数表、频数表或带有各变量列的预格式化表格。预先试行你的表格，以发现含糊的措辞或缺失的类别。

Measure with the highest precision available. Record readings to the nearest division on the scale and estimate one extra digit where possible. Repeat measurements and take an average to reduce random error. For questionnaire data, avoid leading questions and ensure response options are exhaustive and mutually exclusive.

以可用的最高精度进行测量。将读数记录到刻度上最近的刻度线，并在可能的情况下估计一位额外数字。重复测量并取平均值以减少随机误差。对于问卷数据，避免引导性问题，并确保回答选项穷尽且互斥。

6. Recording and Cleaning Data | 记录与清理数据

Transfer your raw data into a structured format, such as a spreadsheet, as soon as possible. Check for obvious errors (transposition mistakes, missing values, impossible values) and correct them against the original records. Document any corrections you make.

尽快将原始数据转移到结构化格式中，如电子表格。检查明显的错误（转置错误、缺失值、不可能的值），并根据原始记录进行更正。记录你所做的任何更正。

If you encounter outliers, do not simply remove them. Investigate whether they are due to measurement error or represent genuine extreme values. If you keep them, explain your reasoning. If you discard them, state the criteria you used. Reliability of conclusions depends heavily on data quality.

如果遇到异常值，不要简单地删除它们。调查它们是由于测量误差还是代表了真正的极端值。如果你保留它们，请解释你的理由。如果你舍弃它们，请说明你使用的标准。结论的可靠性在很大程度上依赖于数据质量。

7. Graphical Representations | 统计图表

Choose the most effective graph to display your data. The table below matches data types to appropriate visuals.

选择最有效的图表来展示你的数据。下表将数据类型与合适的图形匹配。

Data type	Suitable graphs
Categorical (nominal/ordinal)	Bar chart, pie chart, pictogram
Discrete (counts)	Vertical line chart, frequency polygon
Continuous (grouped)	Histogram (with frequency density), cumulative frequency curve, box plot
Bivariate	Scatter graph, time series graph

Every graph must have a title, labelled axes with units, and a consistent scale. In a histogram, remember that the area of each bar is proportional to frequency, so you must plot frequency density = frequency ÷ class width on the vertical axis. For box plots, clearly show the minimum, lower quartile, median, upper quartile, and maximum, and use an appropriate scale.

每个图表必须有标题、带单位的轴标签以及一致的刻度。在直方图中，请记住，条形面积与频率成正比，因此你必须在纵轴上绘制频率密度 = 频率 ÷ 组距。对于箱线图，需清楚显示最小值、下四分位数、中位数、上四分位数和最大值，并使用适当的刻度。

8. Summary Statistics and Measures of Spread | 汇总统计量与离散度量

Calculate averages and measures of spread to summarise your data numerically. Use the arithmetic mean for symmetric data and the median for skewed data or when outliers are present. Always report at least one measure of spread alongside your average.

计算平均数和离散度量以数值方式汇总你的数据。对于对称数据使用算术平均数，对于偏态数据或存在异常值时使用中位数。始终在汇报平均数的同时报告至少一个离散度量。

The range is simple but sensitive to outliers. The interquartile range (IQR) is more robust. The key formulas you need to be able to use and apply are:

极差简单，但对异常值敏感。四分位距 (IQR) 更稳健。你需要能够使用和应用的关键公式有：

Range = Maximum − Minimum

IQR = Q₃ − Q₁

Sample standard deviation s = √[ Σ(xᵢ − x̄)² / (n − 1) ]

When comparing two data sets, quote both a central measure and a measure of spread, e.g. ‘The median reaction time for Group A was 0.35 s with an IQR of 0.12 s, compared to a median of 0.41 s and IQR of 0.09 s for Group B, suggesting Group A was generally faster but more variable.’

比较两个数据集时，要同时引用中心度量和离散度量，例如：“A 组反应时间中位数为 0.35 秒，IQR 为 0.12 秒，而 B 组中位数为 0.41 秒，IQR 为 0.09 秒，这表明 A 组总体上更快，但变异性更大。”

9. Probability and Simulation in Practice | 概率与模拟在实践中的应用

Your investigation may involve chance. You might calculate experimental probabilities from relative frequencies and compare them with theoretical values. For example, if you roll a die 120 times and get a six 28 times, the experimental probability is 28/120 = 0.233, compared to the theoretical 1/6 ≈ 0.167. Discuss reasons for the difference, such as sample size and randomness.

你的调查可能涉及随机性。你可以从相对频率计算实验概率，并将其与理论值比较。例如，如果你掷骰子 120 次，得到 28 次六，那么实验概率是 28/120 = 0.233，而理论概率是 1/6 ≈ 0.167。讨论产生差异的原因，如样本量和随机性。

Simulation can be used to model real‑world processes when direct experimentation is impractical. You could use random digits to simulate coin tosses, traffic flow, or genetic outcomes. Always document the model’s assumptions and test its validity by comparing simulated results with known data.

当直接实验不切实际时，可以使用模拟来模拟真实世界的过程。你可以使用随机数字来模拟抛硬币、交通流量或遗传结果。务必记录模型的假设，并通过将模拟结果与已知数据进行比较来检验其有效性。

10. Drawing Inferences, Conclusions, and Evaluating Findings | 进行推断、得出结论并评估发现

Relate your findings back to the original question or hypothesis. A conclusion should not simply repeat the numbers; it must answer the investigation’s aim, supported by evidence from your data. Use phrases like ‘The data suggests that…’ or ‘There is evidence to support the hypothesis that…’ rather than stating absolute proof.

将你的发现与最初的问题或假设联系起来。结论不应只是重复数字；它必须回答调查的目标，并用数据中的证据加以支持。使用诸如“数据表明……”或“有证据支持……的假设”等措辞，而不是陈述绝对的证明。

Identify any limitations in your method: potential bias, small sample size, measurement imprecision, or uncontrolled variables. Discuss how these might have affected your results and suggest specific improvements for future investigations. This critical evaluation is often what separates higher marks from average ones.

识别你方法中的任何局限性：潜在的偏差、样本量小、测量不精确或未控制的变量。讨论这些因素可能如何影响了你的结果，并对未来的调查提出具体的改进建议。这种批判性评估往往是获得高分与一般分数的区别所在。

11. Reporting Your Investigation | 撰写调查报告

Your final report should be structured logically. A clear structure helps the examiner follow your thinking. Typically, you should include an introduction with clear aims and hypotheses, a method section describing design and sampling, a results section with tables and graphs, an analysis using summary statistics, a conclusion, and an evaluation.

你的最终报告应该具有逻辑结构。清晰的结构有助于考官理解你的思路。通常，你应该包括：带有明确目标和假设的引言，描述设计和抽样方法的方法部分，带有表格和图表的结果部分，使用汇总统计的分析，结论和评估。

Avoid raw data dumps in the main body; place raw data in an appendix if necessary. Label all figures and tables, and refer to them in the text. Write in the third person, past tense, and use statistical vocabulary accurately: ‘mean’, ‘sample’, ‘population’, ‘correlation’, ‘causal’, ‘random’, etc.

避免在正文中堆积原始数据；如有必要，将原始数据放在附录中。标记所有图形和表格，并在正文中引用它们。使用第三人称、过去时态写作，并准确使用统计词汇：“平均数”、“样本”、“总体”、“相关性”、“因果”、“随机”等。

12. Avoiding Common Mistakes in Practical Assessments | 避免实践考核中的常见错误

Many marks are lost through avoidable errors. Watch out for: using a line graph for categorical data instead of a bar chart; plotting frequency instead of frequency density in a histogram with unequal class widths; calculating the mean of grouped data without using midpoints; confusing correlation with causation; and ignoring the context when interpreting results.

许多分数是因可避免的错误而丢失的。要注意：对分类数据使用了折线图而非条形图；在组距不等的直方图中绘制频率而非频率密度；计算分组数据的平均数时未使用组中点；混淆相关性与因果关系；以及在解释结果时忽略背景。

Another frequent weakness is failing to comment on the reliability of data. Even if your calculations are correct, you must discuss how confident you are in the findings. Mention sample size, possible bias, and whether the results can be generalised to a wider population.

另一个常见的问题是未能评论数据的可靠性。即使你的计算正确，你也必须讨论你对发现结果的信心程度。提及样本量、可能的偏差，以及结果是否可以推广到更广泛的总体。

Published by TutorHao | Statistics Revision Series | aleveler.com

更多咨询请联系16621398022（同微信）

WJEC Year 10 Statistics: Experimental & Practical Assessment Essentials | WJEC 十年级统计：实验与实践考核要点