📚 Statistical Report Writing: Framework and Example | 统计报告写作框架与范文
In Year 9 OCR Statistics, you will often be asked to carry out a small-scale investigation and produce a statistical report. Writing a clear and well-structured report is just as important as collecting and analysing the data. This article provides a step-by-step framework for writing a statistical report, together with a sample report to guide you.
在 Year 9 OCR 统计课程中，你经常需要开展一个小型调查并撰写统计报告。撰写一份清晰、结构良好的报告与收集和分析数据同样重要。本文提供了一个撰写统计报告的逐步框架，并附上一份范文来指导你。
1. Understanding the Statistical Project | 理解统计项目
A statistical project is a chance to investigate a real-world question using the data handling cycle. The cycle typically involves: pose a question, collect data, process and present data, analyse and interpret, and evaluate. Every good report follows this structure so the reader can see exactly how you reached your conclusion.
统计项目是运用数据处理周期探究真实世界问题的机会。该周期通常包括：提出问题、收集数据、处理与展示数据、分析与解释、评估。每一份优秀的报告都遵循这一结构，让读者清晰了解你是如何得出结论的。
In OCR Statistics at Year 9, you are expected to carry out a small survey or experiment, use appropriate graphs and averages, and write a report that explains your findings. The report should not just show numbers—it should tell a story with data.
在 Year 9 的 OCR 统计中，你需要完成一个小型调查或实验，使用适当的图表和平均数，并撰写报告来解释发现。报告不应只展示数字——它应当用数据讲述一个故事。
2. Formulating a Research Question | 提出研究问题
Your statistical report starts with a focused question that can be answered with data. A good question is clear, measurable, and relates two or more variables. For example, "Do Year 9 boys spend more time on video games than Year 9 girls?" or "Is there a relationship between hours of sleep and test scores?"
统计报告始于一个可以用数据回答的聚焦问题。一个好问题是清晰、可衡量的，并且涉及两个或更多变量。例如，“九年级男生花在电子游戏上的时间比女生多吗？”或“睡眠时间与考试成绩之间是否存在关系？”
From the question, you can form a hypothesis—a statement you think might be true based on what you already know. For instance, "Year 9 boys spend more time on video games than girls." This gives your investigation a clear direction.
根据问题，你可以形成一个假设——基于你已知信息认为可能成立的陈述。例如，“九年级男生玩电子游戏的时间比女生多。”这为你的调查提供了明确的方向。
3. Planning Data Collection | 规划数据收集
Before you gather any data, write a simple plan. Decide what type of data you need: primary data (collected by yourself) or secondary data (from existing sources). Most school investigations use primary data through questionnaires or observations.
在收集任何数据之前，先写一个简单的计划。确定你需要什么类型的数据：一手数据（你自己收集的）还是二手数据（来自现有来源）。大多数学校调查通过问卷或观察使用一手数据。
Think about sample size—a larger sample gives more reliable results but takes more time. A sample of at least 30 is often a minimum for Year 9. Also, consider how you will avoid bias: use random sampling or stratified sampling if your population has subgroups.
考虑样本量——较大的样本能提供更可靠的结果，但耗时更多。Year 9 通常至少需要 30 个样本。此外，考虑如何避免偏差：如果总体中存在子群，可使用随机抽样或分层抽样。
4. Data Collection Methods | 数据收集方法
Design your data collection tool carefully. If using a questionnaire, keep questions simple and specific. Avoid leading questions like "Don't you agree that exercise is good?" Use closed questions with tick-boxes for easy analysis, or open questions if you need detailed answers.
仔细设计你的数据收集工具。如果使用问卷，问题要简单具体。避免引导性问题，如“难道你不同意锻炼有好处吗？”。使用带复选框的封闭式问题便于分析，如果需详细答案则使用开放式问题。
Record data in a tally chart or a spreadsheet. For example, when asking about hours of sleep per night, your tally might show: "0–4 hours: |||, 5–6 hours: |||| ||, 7–8 hours: |||| |||| |, 9+ hours: ||||". Always note the date, sample size, and any limitations.
用计数表格或电子表格记录数据。例如，询问每晚睡眠小时数时，你的计数可能显示：“0–4 小时：|||，5–6 小时：|||| ||，7–8 小时：|||| |||| |，9小时以上：||||”。始终记录日期、样本量以及任何局限性。
5. Organising and Presenting Data | 整理与展示数据
Once data is collected, organise it into frequency tables. Group continuous data into equal intervals where sensible. For example, heights could be grouped as 140–149 cm, 150–159 cm, etc. Include columns for frequency, and if needed, cumulative frequency.
收集数据后，将其整理成频数表。合理地将连续数据分组成等距区间。例如，身高可分为 140–149 厘米、150–159 厘米等。根据需要包含频数列，以及累积频数。
Choose appropriate graphs. For comparing categories, use bar charts or pictograms. For showing distributions, use histograms (with equal class widths) or frequency polygons. To show relationships between two variables, scatter graphs are ideal. Always label axes, give a title, and use a key if needed.
选择合适的图表。比较类别时，使用条形图或象形图。展示分布时，使用直方图（等组距）或频数折线图。要显示两个变量间的关系，散点图最合适。始终标注坐标轴、给出标题，并在需要时使用图例。
6. Descriptive Statistics and Charts | 描述统计与图表
Descriptive statistics summarise your data with numbers. Calculate averages: the mean (sum of all values divided by n), the median (middle value when ordered), and the mode (most frequent value). Also, measure spread using the range (maximum – minimum) or the interquartile range (IQR = Q₃ – Q₁).
描述统计用数字概括数据。计算平均数：均值（所有值之和除以 n）、中位数（排序后中间的值）和众数（出现最频繁的值）。此外，用极差（最大值 – 最小值）或四分位距（IQR = Q₃ – Q₁）衡量离散程度。
If you are investigating a relationship, you might draw a line of best fit on a scatter graph and describe the correlation as positive, negative, or none. For Year 9, you do not need to calculate the correlation coefficient r exactly, but you can discuss the strength of the relationship.
如果你在探究关系，可以在散点图上画出最佳拟合线，并描述相关性为正相关、负相关或无相关。对于 Year 9，你无需精确计算相关系数 r，但可以讨论关系的强弱。
Mean = (Σx) / n Range = xₘₐₓ – xₘᵢₙ IQR = Q₃ – Q₁
7. Analysing Data | 分析数据
Analysis goes beyond describing numbers—it explains what the data means in relation to your question. Compare the averages and spreads between groups. For example, if boys' mean gaming time is 2.5 hours and girls' is 1.8 hours, note the difference and consider whether it supports your hypothesis.
分析不仅仅描述数字——它解释数据与你的问题有何关联。比较不同组之间的平均数和离散度。例如，如果男生游戏时间的均值为 2.5 小时，女生为 1.8 小时，注意这一差异并思考它是否支持你的假设。
Comment on unusual values (outliers) and how they affect the mean. If a scatter graph shows a downward trend, say that one variable tends to decrease as the other increases. Use phrases like "the data suggests", "this indicates". Never claim proof unless you have conducted a controlled experiment.
评论异常值（离群点）及其对均值的影响。如果散点图显示下降趋势，说明一个变量随着另一个变量的增加而减少。使用诸如“数据表明”“这显示出”的表述。除非你进行了对照实验，否则永远不要声称证明了因果关系。
8. Drawing Conclusions | 得出结论
Your conclusion should directly answer the research question and state whether the hypothesis was supported. Summarise the key findings using numbers. For instance, "Year 9 boys in our sample spent on average 0.7 hours more on video games than girls, suggesting a gender difference in gaming habits."
你的结论应当直接回答研究问题，并说明假设是否得到支持。用数字总结关键发现。例如，“在我们的样本中，九年级男生玩电子游戏的时间平均比女生多 0.7 小时，表明游戏习惯存在性别差异。”
Do not introduce new data here. Keep it brief and linked to the evidence. If the results were not as expected, say so honestly—a negative result is still a valid finding. Mention any patterns or surprises you noticed during analysis.
此处不要引入新数据。保持简洁，并与证据挂钩。如果结果不如预期，也如实说明——阴性结果仍然是有效的发现。提及你在分析过程中注意到的任何模式或意外现象。
9. Evaluating the Process | 评估过程
Evaluation reflects on how well the investigation went. Discuss limitations: was the sample size large enough? Was it representative? Could the way questions were asked have influenced answers? Identify any sources of bias and explain how they might affect the results.
评估反思调查进行得如何。讨论局限性：样本量是否足够大？是否具有代表性？提问方式是否影响了回答？查明任何偏差来源，并解释它们可能如何影响结果。
Suggest improvements for a future study. For example, "Next time, I would use a stratified sample to ensure equal numbers of boys and girls, and ask about weekend and weekday gaming separately." This shows you are thinking like a real statistician.
为未来的研究提出改进建议。例如，“下一次，我将使用分层抽样以确保男女生人数相等，并分开询问周末和工作日的游戏时间。”这表明你在像真正的统计学家一样思考。
10. Sample Report | 报告范文
Title: An Investigation into the Relationship between Social Media Time and Sleep Hours Among Year 9 Students
标题：关于九年级学生社交媒体时间与睡眠时间关系的调查
Introduction (English): I wanted to find out if spending more time on social media is linked to getting less sleep. My hypothesis was: "Year 9 students who use social media for more hours per night tend to sleep fewer hours." This is important because sleep affects concentration at school.
引言（中文）：我想了解花更多时间在社交媒体上是否与睡眠减少有关。我的假设是：“每晚使用社交媒体时间更长的九年级学生往往睡眠时间更少。”这很重要，因为睡眠影响在校注意力。
Methodology (English): I used a questionnaire to collect primary data from 40 Year 9 students (20 boys, 20 girls) at my school. I asked two questions: "On average, how many hours per night do you use social media?" and "How many hours of sleep do you usually get on a school night?" I recorded answers in a tally and then a spreadsheet.
方法（中文）：我使用问卷收集了本校 40 名九年级学生（20 名男生，20 名女生）的一手数据。我问了两个问题：“你平均每晚使用社交媒体多少小时？”和“你在上学日晚上通常睡多少小时？”我先把答案记录在计数表中，然后录入电子表格。
Results (English): The table below shows a summary of the data.
结果（中文）：下表显示了数据摘要。
| Social Media (hours/night) | Mean Sleep (hours) | Median Sleep (hours) | Number of Students |
|---|---|---|---|
| 0–1 | 8.5 | 8.5 | 10 |
| 1–2 | 7.8 | 8.0 | 12 |
| 2–3 | 7.2 | 7.0 | 10 |
| 3 or more | 6.3 | 6.5 | 8 |
The scatter graph showed a negative correlation. As social media hours increased, sleep hours tended to decrease. I drew a line of best fit that sloping downward. The range of sleep hours in the highest social media group was 5.0 to 7.5 hours, compared to 7.5 to 9.5 hours in the lowest group.
散点图显示负相关。随着社交媒体小时数增加，睡眠小时数趋于减少。我画了一条向下倾斜的最佳拟合线。社交媒体使用时间最高组的睡眠时间极差为 5.0 至 7.5 小时，而最低组为 7.5 至 9.5 小时。
Analysis (English): The mean sleep time drops by about 0.6–0.7 hours for each extra hour of social media. This suggests a fairly strong negative relationship. There was one outlier—a student who reported 4 hours social media and still 8 hours sleep, but this did not change the overall trend. The median sleep times confirm the pattern.
分析（中文）：社交媒体每增加一小时，平均睡眠时间大约下降 0.6–0.7 小时。这表明存在相当强的负关系。有一个离群值——一名学生报告了 4 小时社交媒体但仍睡 8 小时，但这并未改变总体趋势。睡眠时间中位数证实了这一模式。
Conclusion (English): My hypothesis was supported. Year 9 students who use social media for more hours per night tend to get less sleep. The mean sleep difference between the lowest and highest groups was over 2 hours. This could affect their alertness in school.
结论（中文）：我的假设得到支持。每晚使用社交媒体时间更长的九年级学生往往睡眠更少。最低与最高组之间的平均睡眠差异超过 2 小时。这可能影响他们在校的清醒度。
Evaluation (English): The sample was only from one school, so results may not apply everywhere. I noticed some students may have rounded their hours. Next time I would use a larger sample and include weekend sleep data. I would also ask if screens were used right before sleep, as that might be a factor.
评估（中文）：样本仅来自一所学校，因此结果可能不具普遍性。我注意到有些学生可能把小时数凑整了。下一次我会使用更大的样本，并包含周末睡眠数据。我还会询问是否在睡前使用屏幕，因为这可能是一个因素。
This sample shows how a full report can be written concisely while following the statistical cycle. Adapt it to your own investigation, but always present your own data and reasoning clearly.
这个范本展示了如何遵循统计周期简洁地写出一份完整的报告。请根据自己的调查进行调整，但始终清晰地呈现你自己的数据和推理。
Published by TutorHao | Statistics Revision Series | aleveler.com
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