📚 Year 12 SQA Statistics: Key Points for Experimental / Practical Assessments | SQA 统计：实验/实践考核要点
In the SQA Statistics course at Year 12 level, the practical or experimental assessment component requires you to plan, carry out, and interpret a statistical investigation. You need to demonstrate your ability to design a study, collect data responsibly, apply appropriate statistical techniques, and draw valid conclusions. This article outlines the essential points you must master to excel in the practical assessment.
在Year 12 SQA统计课程中，实验或实践考核部分要求你规划、实施并解释一项统计调查。你需要展示设计研究、负责任地收集数据、应用适当的统计技术并得出有效结论的能力。本文概述了在实践考核中取得优异成绩必须掌握的核心要点。
1. Understanding the Assessment Objectives | 理解考核目标
The SQA Statistics practical assessment evaluates your competency in several key areas: formulating a clear research question, selecting an appropriate data collection method, applying statistical techniques correctly, and communicating findings effectively. You are expected to work as an independent investigator, showing critical thinking at every stage.
SQA统计实践考核评估你在几个关键领域的能力：提出明确的研究问题、选择合适的数据收集方法、正确应用统计技术、有效传达研究发现。你应作为独立研究者开展工作，在每个阶段展示批判性思维。
The marking scheme often allocates marks for the process of planning, the execution of data handling, the accuracy of calculations, the use of software, and the quality of your written report. You must be familiar with the specific assessment criteria provided by SQA.
评分方案通常将分数分配给规划过程、数据处理执行、计算准确性、软件使用以及书面报告的质量。你必须熟悉SQA提供的具体评估标准。
2. Formulating a Research Question | 制定研究问题
A good investigation starts with a clear, focused, and answerable research question. Avoid vague questions. Instead, operationalise your variables so that they can be measured or observed. For example, ‘Does the amount of revision affect exam performance?’ can be refined to ‘Is there a positive correlation between the number of hours spent revising and the percentage mark achieved in a statistics test?’
一个好的调查始于清晰、聚焦且可回答的研究问题。避免模糊的问题。应将变量操作化，使其能够被测量或观察。例如，“复习量是否影响考试成绩？”可以细化为“复习小时数与统计测试得分百分比之间是否存在正相关？”。
Your research question should be linked to a hypothesis that can be tested statistically. State the null hypothesis (H₀) and the alternative hypothesis (H₁ or Hₐ) explicitly, using symbols where appropriate, such as H₀: μ = 0 versus H₁: μ ≠ 0.
你的研究问题应与可通过统计检验的假设相关联。明确陈述原假设（H₀）和备择假设（H₁或Hₐ），在适当地方使用符号，例如 H₀: μ = 0 对比 H₁: μ ≠ 0。
3. Planning Data Collection | 规划数据收集
Decide whether you will use primary data (collected by yourself) or secondary data (existing datasets). If primary, design your instruments carefully, such as questionnaires, measurement protocols, or observational checklists. Pilot your data collection tool to identify any flaws before the main study.
决定使用一手数据（自己收集）还是二手数据（现有数据集）。如果是一手数据，精心设计你的工具，例如问卷、测量方案或观察清单。在正式研究之前，先试行你的数据收集工具以发现任何缺陷。
Consider ethical issues, including informed consent, anonymity, and data protection. In SQA assessments, you may need to include a brief ethics statement in your report. Clearly define your target population and sample size, explaining why your sample size is adequate for the statistical tests you plan to use.
考虑伦理问题，包括知情同意、匿名和数据保护。在SQA考核中，你可能需要在报告中包含简短的伦理声明。明确定义你的目标总体和样本量，解释为什么样本量对你计划使用的统计检验是足够的。
4. Sampling Techniques | 抽样技术
Demonstrate understanding of different sampling methods: simple random, stratified, systematic, cluster, and quota sampling. Explain the advantages and disadvantages of each in the context of your investigation. For instance, stratified sampling ensures representation of subgroups, but it requires knowledge of population proportions.
展示对不同抽样方法的理解：简单随机、分层、系统、整群和配额抽样。结合你的调查背景，解释每种方法的优缺点。例如，分层抽样确保子群体的代表性，但需要了解总体比例。
If using a random sample, describe how randomness was achieved, e.g., using random number tables or software. Discuss potential sampling bias and how you minimised it. A common pitfall is using convenience sampling without acknowledging its limitations.
如果使用随机样本，描述随机性是如何实现的，例如使用随机数表或软件。讨论潜在的抽样偏差以及你是如何将其最小化的。常见陷阱是使用便利抽样但不承认其局限性。
5. Experimental Design Principles | 实验设计原则
If your investigation involves an experiment rather than an observational study, apply core principles: control, randomisation, and replication. Use a control group where relevant to establish a baseline for comparison. Randomly allocate subjects to treatment and control groups to minimise confounding variables.
如果你的调查涉及实验而非观察性研究，应用核心原则：控制、随机化和重复。在相关情况下使用对照组以建立比较基线。将受试者随机分配到处理组和对照组，以最小化混杂变量。
Explain how you controlled extraneous variables. For example, if testing the effect of a new teaching method on test scores, you might need to control for prior attainment, time of day, and instructor. Blocking or matched pairs designs can be used to reduce variability.
解释你如何控制无关变量。例如，如果测试一种新教学方法对考试成绩的影响，你可能需要控制先前成绩、一天中的时间和授课教师。可以使用区组或配对设计来减少变异性。
6. Data Entry and Cleaning | 数据录入与清理
Accurate data entry is crucial. Use spreadsheet software like Excel or statistical packages such as Minitab. Create a clear coding system for categorical variables (e.g., 1 = Male, 2 = Female). Check for data entry errors by verifying a random sample of records and by conducting range checks or logical consistency checks.
准确的数据录入至关重要。使用电子表格软件（如Excel）或统计软件包（如Minitab）。为分类变量创建清晰的编码系统（例如 1 = 男，2 = 女）。通过验证随机抽取的记录样本、进行范围检查或逻辑一致性检查来核实数据录入错误。
Deal with missing data appropriately. State how you handled missing values – whether you excluded cases, used imputation, or recorded them as such. In your report, mention any data cleaning steps so that the examiner knows you have considered data quality.
适当处理缺失数据。说明你如何处理缺失值——是排除个案、使用插补还是如实记录。在报告中提及任何数据清理步骤，以便考官知道你已考虑数据质量。
7. Descriptive Statistics and Graphical Displays | 描述性统计与图形展示
Produce relevant descriptive statistics: mean, median, mode, range, interquartile range, variance, and standard deviation. For a dataset, the sample mean is calculated as:
x̄ = Σxᵢ ÷ n
and the sample standard deviation as:
s = √[ Σ(xᵢ − x̄)² ÷ (n − 1) ]
计算相关的描述性统计量：均值、中位数、众数、极差、四分位距、方差和标准差。对于一组数据，样本均值计算公式为：
x̄ = Σxᵢ ÷ n
样本标准差为：
s = √[ Σ(xᵢ − x̄)² ÷ (n − 1) ]
Choose appropriate graphs: histograms for continuous data, box plots for comparing distributions, scatter plots for bivariate relationships, and bar charts for categorical data. Ensure your graphs are fully labelled with titles, axis labels, and units, and include a key if necessary.
选择合适的图形：连续数据用直方图，比较分布用箱线图，双变量关系用散点图，分类数据用条形图。确保图形完整标注标题、轴标签和单位，必要时包含图例。
8. Inferential Statistics and Hypothesis Testing | 推断统计与假设检验
Select the correct inferential test based on your data type and research question. Common tests include the t-test for comparing two means, chi-squared test for independence, correlation and regression for bivariate continuous data. State the assumptions of the test and check whether they are met (e.g., normality, homogeneity of variance).
根据数据类型和研究问题选择正确的推断检验。常见检验包括用于比较两个均值的t检验、用于独立性的卡方检验、用于双变量连续数据的相关和回归。陈述检验的假设并检查这些假设是否满足（例如正态性、方差齐性）。
Report the test statistic, degrees of freedom, and p-value clearly. Compare the p-value to the significance level α, typically 0.05, and make a decision regarding the null hypothesis. Use clear wording: ‘Since p = 0.032 < 0.05, we reject H₀ and conclude that there is a statistically significant difference.'
清晰地报告检验统计量、自由度和p值。将p值与显著性水平α（通常为0.05）进行比较，并做出关于原假设的决定。使用清晰表述：“由于 p = 0.032 < 0.05，我们拒绝 H₀，并得出结论：存在统计学显著差异。”
If using software, include relevant output snippets (with permission) and explain how you interpreted them. Avoid simply pasting large tables without commentary.
如果使用软件，包含相关的输出片段（在允许的情况下）并解释你是如何解读的。避免仅粘贴大表格而不加评论。
9. Interpretation and Conclusions | 解释与结论
Interpret your results in the context of the original research question. Do not just state numerical outcomes; explain what they mean in practical terms. Discuss the strength of evidence, effect sizes (e.g., Cohen’s d, correlation coefficient r), and confidence intervals where appropriate.
在原始研究问题的背景下解释你的结果。不要只陈述数值结果；要解释它们在实践层面的含义。在适当情况下讨论证据强度、效应量（如Cohen’s d、相关系数 r）和置信区间。
Acknowledge limitations of your study, such as small sample size, potential sources of bias, or uncontrolled variables. Suggest how the investigation could be improved or extended in future work. This demonstrates critical evaluation, which is often awarded high marks.
承认研究的局限性，如样本量小、潜在偏差来源或未控制的变量。提出在未来工作中如何改进或扩展调查。这展示了批判性评价，通常能获得高分。
10. Written Report and Communication | 书面报告与沟通
Structure your report logically: introduction, methodology, results, discussion, conclusion, and references. Use clear headings and consistent formatting. Write in an objective, scientific style, avoiding personal pronouns when describing the process. Present data using tables and figures effectively, with informative captions.
逻辑清晰地组织报告：引言、方法、结果、讨论、结论和参考文献。使用清晰的标题和一致的格式。以客观、科学的风格撰写，描述过程时避免使用人称代词。有效使用表格和图形呈现数据，并配有信息丰富的标题。
Proofread your report for spelling and grammar errors, and ensure all statistical terminology is used correctly. Refer to SQA guidelines on referencing if you use secondary data sources. A well-presented report reflects professionalism and can influence the overall impression on the examiner.
仔细校对报告，消除拼写和语法错误，并确保所有统计术语使用正确。如果使用二手数据源，请参考SQA的引用指南。一份呈现良好的报告体现了专业性，并能影响考官的整体印象。
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