📚 Pre-U OCR Statistics: Practical Investigation Key Points | Pre-U OCR 统计：实践考核要点
The OCR Pre-U Statistics qualification includes a vital internally assessed component – the Statistical Investigation (Unit 2). This practical task challenges you to demonstrate independent statistical enquiry, from formulating a hypothesis to presenting a professional report. Success requires careful planning, rigorous data handling, and insightful analysis.
OCR Pre-U 统计学资格包含一个至关重要的内部评估部分——统计调查（第二单元）。这项实践任务要求你展示独立的统计探究能力，从提出假设到呈现专业报告。成功需要周详的计划、严格的数据处理以及深入的分析。
1. Understanding the Practical Investigation | 理解实践调查考核
This unit accounts for a significant proportion of your final grade and tests a range of skills: framing a research question, devising a sampling strategy, collecting or sourcing data, applying appropriate statistical methods, and interpreting results in context. Markers look for evidence of genuine statistical thinking rather than just computation.
该单元占最终成绩的很大比重，并测试一系列技能：构建研究问题、设计抽样策略、收集或获取数据、应用适当的统计方法，以及在具体情境下解释结果。评分者看重真正的统计思维证据，而不仅仅是计算。
2. Choosing a Research Question | 选择一个研究问题
Begin by identifying a topic that intrigues you and can be investigated using quantitative data. Your question must be focused and testable. For example, “Is there a difference in the average daily screen time between Year 10 and Year 12 students?” is a good starting point. Steer clear of overly broad or trivial questions.
首先确定一个你感兴趣且能用量化数据研究的主题。你的问题必须集中且可检验。例如，“10年级和12年级学生的日均屏幕时间是否存在差异？”是一个好的起点。避免过于宽泛或琐碎的问题。
Refine your initial idea by checking if existing datasets or feasible data collection methods can provide the necessary variables. A well-crafted question guides the entire investigation.
通过检查现有数据集或可行的数据收集方法是否能提供必要的变量，来完善你的初始想法。一个精心设计的问题会引导整个调查。
3. Planning the Investigation | 规划调查
A robust plan includes defining the population, selecting a sampling method (random, stratified, etc.), determining sample size, and listing the variables to be recorded. Consider potential sources of bias and how you will minimise them.
一个稳健的计划包括界定总体、选择抽样方法（随机、分层等）、确定样本量，以及罗列要记录的变量。考虑潜在的偏误来源以及如何将其最小化。
For secondary data, document the source clearly and assess its credibility. Pre-register your analysis plan to avoid p-hacking or data dredging.
对于次级数据，清晰记录来源并评估其可信度。预先注册你的分析计划，以避免p值操纵或数据挖掘。
4. Data Collection Methods | 数据收集方法
You may collect primary data through surveys, experiments, or observations. If doing a survey, pilot your questions to ensure clarity. For experimental data, use randomisation and control groups where possible.
你可以通过调查、实验或观察收集初级数据。如果做问卷调查，先试点你的问题以确保清晰。对于实验数据，尽可能使用随机化和对照组。
Secondary data from reputable sources (e.g., national statistics, published research) can save time, but you must verify its relevance and reliability. Always cite the dataset accurately.
来自可靠来源（如国家统计局、已发表的研究）的次级数据可以节省时间，但你必须验证其相关性和可靠性。始终准确引用数据集。
5. Ensuring Reliability and Validity | 确保可靠性和有效性
Reliability refers to consistency – would you get similar results if the study were repeated? Validity concerns whether you are measuring what you intend to measure. Address both by using well-defined variables, standardised measurement instruments, and random sampling.
信度指一致性——如果你重复该研究，是否会得到相似的结果？效度涉及你是否在测量你意图测量的东西。通过使用定义明确的变量、标准化的测量工具和随机抽样来解决这两方面。
Internal validity can be threatened by confounding variables. Discuss how you controlled for them, e.g., by restricting the sample or using statistical adjustments.
内部效度可能会受到混杂变量的威胁。讨论你如何控制它们，例如通过限制样本或使用统计调整。
6. Data Processing and Cleaning | 数据处理与清洗
Before analysis, clean your dataset: check for missing values, outliers, and impossible entries. Decide on a strategy for missing data (e.g., listwise deletion, imputation) and justify your choice.
在分析之前，清洗你的数据集：检查缺失值、异常值和不可能的值。决定缺失数据的处理策略（如整例删除、插补）并说明理由。
Create a codebook or data dictionary so that every variable is clearly labelled. This prevents confusion and helps you document transformations like log-scaling or categorisation.
创建一个编码簿或数据字典，以便每个变量都有清晰的标签。这可以防止混淆，并帮助你记录如对数转换或分类等变换。
7. Exploratory Data Analysis | 探索性数据分析
Start by computing summary statistics: mean (x̄), median, standard deviation (s), interquartile range (IQR), and five-number summary. Use graphical displays: histograms to show distribution shape, box plots to compare groups, and scatter plots to examine relationships.
首先计算汇总统计量：均值(x̄)、中位数、标准差(s)、四分位距(IQR)和五数概括。使用图形展示：直方图显示分布形状，箱线图比较组间差异，散点图检查关系。
Comment on patterns, skewness, gaps, or unusual observations. This step helps you choose appropriate inferential methods and detect violations of assumptions (e.g., normality).
评论模式、偏态、间隙或不寻常的观测值。这一步有助于你选择合适的推断方法，并检测对假设（如
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