📚 Year 11 SQA Statistics: Experimental and Practical Assessment Essentials | 实验/实践考核要点
In SQA Statistics, the experimental or practical assessment is your chance to show how statistical thinking is applied to real-world problems. This article walks you through the assessment criteria, planning, data handling, analysis and evaluation, giving you the key points you must demonstrate to succeed.
在 SQA 统计课程中，实验或实践考核是你展示如何将统计思维应用于实际问题的机会。本文带你梳理考核标准、计划、数据处理、分析和评估，给出你必须展示的关键要点，助你顺利通过考核。
1. Understanding the Assessment Criteria | 理解考核标准
The practical assessment in SQA Statistics is built around the statistical enquiry cycle. You are expected to plan an investigation, collect or source data, process and present findings, then interpret results and evaluate the whole process critically.
SQA 统计的实践考核围绕统计探究循环展开。考官期望你规划一项调查，收集或获取数据，处理并呈现结果，然后批判性地解释结果并评估整个过程。
Marks are typically awarded for clear aims, appropriate methodology, accurate use of statistical techniques, meaningful diagrams, and a well-reasoned conclusion that links back to the original question. Your evaluation must identify strengths, weaknesses and realistic improvements.
评分通常围绕清晰的目标、恰当的方法、统计技术的准确使用、有意义的图表以及紧扣原始问题的合理结论展开。你的评估必须指出优点、不足和切合实际的改进建议。
2. Formulating a Research Question | 提出研究问题
A successful investigation starts with a focused, testable question. Instead of 'How does exercise affect health?', refine it to 'Is there a relationship between the number of hours of weekly exercise and resting heart rate for Year 11 students?' This clarifies your variables.
一项成功的调查始于一个具体、可检验的问题。不要提“运动如何影响健康？”，而是细化为“11 年级学生每周运动小时数与静息心率之间是否存在关系？”这样能理清变量。
You must identify the independent variable (the one you change or compare), the dependent variable (the one you measure), and the control variables you will keep constant to ensure a fair test. Stating these explicitly shows sound experimental planning.
你必须明确自变量（你改变或比较的变量）、因变量（你测量的变量）以及为了确保公平测试而保持恒定的控制变量。清晰陈述这些能体现扎实的实验规划能力。
3. Planning the Experiment | 实验计划
A strong plan includes a hypothesis (a prediction of what you think will happen), a list of equipment, and a step-by-step method. If you are using a survey, the questionnaire must be designed to avoid leading questions and ambiguous wording.
一个好的计划包括一条假设（预测你认为会发生什么）、一份设备清单和分步方法。如果使用问卷调查，问卷设计必须避免诱导性问题和模糊的措辞。
Safety considerations and ethical issues should be mentioned, especially when human subjects are involved. Planning how data will be recorded in advance (tables, tally sheets) saves time and reduces recording errors during the practical phase.
应提及安全问题和伦理考虑，尤其是在涉及受试者时。提前规划好数据记录方式（表格、划记表）能在实践阶段节省时间并减少记录错误。
4. Data Collection Methods | 数据收集方法
You can use primary data (collected by yourself) or secondary data (sourced from reliable databases, websites or textbooks). Primary data gives you full control over how it is gathered, which is valuable for an investigation involving measurement or timing.
你可以使用一手数据（自己收集）或二手数据（来自可靠的数据库、网站或教材）。一手数据让你完全控制收集过程，这对涉及测量或计时的调查很有价值。
No matter the source, data must be collected systematically. When measuring, read instruments at eye level and record readings to an appropriate degree of precision. Repeated measurements can help reduce random uncertainty and give a more reliable central value.
无论来源如何，数据都必须系统性地收集。测量时应平视仪器读数，并以合适的精度记录数值。重复测量有助于减少随机不确定性，得出更可靠的集中值。
5. Sampling Techniques | 抽样技术
The sample must represent the population you are investigating. Random sampling ensures every member has an equal chance of being selected, which reduces bias. Stratified sampling divides the population into subgroups and draws samples proportionally from each.
样本必须能代表你所调查的总体。随机抽样确保每个成员有均等的选中机会，从而减少偏差。分层抽样将总体分成子种群，然后从每个子种群按比例抽取样本。
Systematic sampling (selecting every kth element) can be practical but may introduce patterns. Avoid convenience sampling for anything that is assessed, unless you carefully discuss its limitations. Always record your sampling method and justify your choice.
系统抽样（每隔 k 个元素选取）可能很实用，但可能会引入规律性。除非仔细讨论其局限性，否则在考核中应避免便利抽样。始终记录你的抽样方法并说明选择理由。
6. Dealing with Measurement and Errors | 处理测量与误差
Every measurement carries some uncertainty. Systematic errors (e.g. a zero error on a stopwatch) affect all readings in a predictable way and can often be corrected. Random errors arise from unpredictable fluctuations and can be minimised by repeating measurements and using an average.
每次测量都带有一定的不确定性。系统误差（例如秒表的零点误差）会以可预测的方式影响所有读数，通常可以校正。随机误差源自不可预测的波动，可以通过重复测量并取平均值来最小化。
mber of significant figures shows awareness of measurement precision. Blunders, such as misreading a scale, should be identified before analysis begins.
在表格中记录数据时，务必注明单位和不确定度范围（例如 15.2 ± 0.1 cm）。使用正确的有效数字位数能体现你对测量精度的意识。诸如读错刻度一类的失误，应在分析开始前识别出来。
7. Organising and Recording Data | 数据组织与记录
Present raw data in clear, well-labelled tables. The header row should include the description of each variable and its unit. For example, 'Height (cm)' or 'Time (s)'. Tally charts are useful when you are counting frequencies for categorical data.
将原始数据呈现在清晰、标注完善的表格中。表头行应包括每个变量的描述及其单位，例如“身高 (cm)”或“时间 (s)”。当统计分类数据的频数时，划记表非常有用。
Ordered lists and grouped frequency tables help detect patterns and outliers. When grouping, choose class intervals that are equal in width and cover the full range without overlap. State the grouping rule at the foot of the table.
排序列表和分组频数表有助于发现规律和异常值。分组时应选择等宽且涵盖全部范围而不重叠的组距。在表格底部注明分组规则。
8. Graphical Representation | 图形表征
Selecting the right graph is critical. Use a bar chart for discrete categories, a histogram for continuous grouped data, and a scatter diagram to explore relationships between two numerical variables. Line graphs are appropriate for changes over time.
选择合适的图形至关重要。离散类别用条形图，连续分组数据用直方图，探索两个数值变量关系用散点图。随时间变化的趋势适合用折线图。
Every graph needs a descriptive title, labelled axes with units, and a clear scale. If a scatter diagram suggests correlation, you may add a line of best fit, but only if the correlation is strong enough to justify it. Outliers should be clearly visible and mentioned.
每张图都需要描述性标题、标注单位并带有清晰刻度的坐标轴。如果散点图显示相关关系，可以添加最佳拟合线，但仅当相关性足够强时才有意义。异常值应清晰可见并予以说明。
9. Calculating Statistical Measures | 计算统计量
Descriptive statistics summarise your data. For a central tendency, choose the mean, median or mode according to the distribution. The mean is suitable for symmetric data, while the median is robust against outliers. The mode is useful for categorical data.
描述性统计量能概括数据特征。集中趋势可选择平均值、中位数或众数，视分布情况而定。平均值适用于对称数据，中位数对异常值稳健，众数则适用于分类数据。
Measures of spread, such as range, interquartile range and standard deviation, tell you how spread out the data are. In SQA Statistics, you should be able to compute these using a calculator and interpret the results: a smaller standard deviation means less variability.
离散程度量度，如极差、四分位距和标准差，能反映数据分散的程度。在 SQA 统计中，你应该能够使用计算器计算这些值并解释结果：标准差越小意味着变异性越小。
Sample mean: x̄ = (∑xᵢ)/n ｜ Sample standard deviation: s = √[∑(xᵢ - x̄)²/(n-1)]
10. Interpreting Results and Drawing Conclusions | 解释结果并得出结论
Go back to your original research question and hypothesis. Do your findings support the hypothesis or not? Avoid claiming 'proof' – statistical evidence can only suggest a trend or relationship, never absolute proof.
回到原始研究问题和假设。你的发现是否支持假设？避免声称“证明”——统计证据只能表明趋势或关系，绝不能作为绝对证明。
Comment on the strength of any correlation using terms like 'weak', 'moderate' or 'strong', and describe the pattern as positive, negative or non-linear. If you identify a difference between two groups, discuss whether it is likely to be meaningful or just down to chance.
评价相关性的强度时使用“弱”、“中等”或“强”等术语，并描述模式为正相关、负相关或非线性。如果你发现两组之间的差异，讨论该差异是否很可能有实际意义，还是仅仅出于偶然。
11. Evaluating the Experiment | 评估实验
Reflect on the reliability and validity of your investigation. Reliability refers to the consistency of results – would you get similar findings if you repeated it? Validity asks whether you genuinely measured what you set out to measure.
反思调查的可靠性和有效性。可靠性指结果的一致性——如果重复实验，是否会得到相似的结果？有效性则质疑你是否真正测量了计划测量的内容。
Identify specific sources of error or bias: was the sample size too small, were there uncontrolled variables, did the measuring instrument have limitations? Then propose concrete improvements, such as increasing sample size, automating timing, or using a blind trial.
指出具体的误差或偏差来源：样本量是否太小，是否存在未控制的变量，测量仪器是否有局限？然后提出具体的改进措施，例如增大样本量、采用自动计时、或使用盲法试验。
Discussing how you could extend the investigation to explore related questions shows higher-order thinking and can lift your evaluation mark significantly.
讨论如何扩展调查以探究相关问题，能体现高阶思维，从而显著提升评估得分。
12. Report Writing and Presentation | 报告撰写与展示
A well-structured report follows a logical flow: introduction, methodology, results, analysis, conclusion and evaluation. Use headings, subheadings and neat presentation to make your report easy to navigate.
一份结构良好的报告应遵循逻辑顺序：引言、方法、结果、分析、结论和评估。使用标题、小标题和整洁的排版，让报告易于浏览。
Embed graphs and tables close to where they are discussed, and refer to them in the text (e.g. 'As shown in Figure 1…'). Use precise statistical vocabulary and link every statement to the evidence. Finally, proofread for clarity, spelling and correct units.
将图表嵌入到讨论它们的文字附近，并在文中引用（例如“如图 1 所示……”）。使用精确的统计术语，并使每条陈述都与证据挂钩。最后，仔细校对，确保清晰、拼写正确且单位无误。
If you used secondary data, cite the sources properly. Reflecting on the entire process in your evaluation shows that you can think as a practising statistician, which is exactly what the SQA examiners are looking for.
如果使用了二手数据，要正确引用来源。在评估中反思整个过程，能表明你能像一名实践中的统计师那样思考，这正是 SQA 考官所期望的。
Published by TutorHao | Statistics Revision Series | aleveler.com
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