Year 7 SQA Statistics: Experimental and Practical Assessment Key Points | 七年级 SQA 统计：实验与实践考核要点

📚 Year 7 SQA Statistics: Experimental and Practical Assessment Key Points | 七年级 SQA 统计：实验与实践考核要点

This revision guide breaks down the essential skills and knowledge needed for the SQA practical statistics assessment in Year 7 (S1). You will be expected to plan and conduct a statistical investigation, then communicate your findings clearly, using appropriate graphs and calculations. The process is just as important as the final answer, so you must show your reasoning and evaluate your method. Understanding each step – from framing a question to reflecting on possible improvements – will help you succeed in this practical task.

本复习指南详细阐述了七年级（S1）SQA 统计实践考核所需的关键技能和知识。你需要计划并实施一项统计调查，然后使用合适的图表和计算方法清晰地展示你的发现。过程与最终答案同样重要，因此你必须展示推理过程并评价自己的方法。理解从提出问题到反思改进措施的每一步，将助你在实践任务中取得成功。

1. Understanding the Purpose of Practical Statistics | 理解统计实践的目的

Statistics is the science of collecting, organising, analysing, and interpreting data to answer questions about the world. In your S1 practical assessment, you will demonstrate that you can follow this cycle. The focus is not on memorising formulas, but on applying them to real data you have gathered.

统计学是一门关于收集、整理、分析和解释数据以回答现实世界问题的科学。在 S1 实践考核中，你要展示自己能够遵循这一循环。重点不在于死记公式，而在于将公式应用到自己收集的真实数据上。

The SQA expects you to show curiosity, choose suitable methods, and support your conclusions with evidence. A well-structured investigation will earn higher marks than a rushed one, even if the numbers are similar.

SQA 期望你展现好奇心，选择合适的方法，并用证据支持结论。一个结构良好的调查会比匆忙完成调查获得更高分数，即使最终数字相似。

2. Formulating a Question and Hypothesis | 提出问题与假设

Every investigation begins with a statistical question. This question must be clear, specific, and something that can be answered with data. For instance, “What is the most common way S1 pupils travel to school?” is better than an open question like “Tell me about transport.”

每一次调查都始于一个统计问题。这个问题必须清晰、具体，并且能够用数据回答。例如，“S1 学生上学最常用的交通方式是什么？”就比“说说交通情况”这样的开放问题更好。

A hypothesis is a prediction based on your initial thoughts or observations. It should state what you expect to find. A good hypothesis is testable: “Pupils who eat breakfast score higher in a simple spelling test” can be checked by collecting data on breakfast habits and test results.

假设是基于初步想法或观察做出的预测，应说明你预期会得到什么结果。一个好的假设是可检验的：“吃早餐的学生在简单拼写测验中得分更高”这一说法，可以通过收集早餐习惯和测验成绩的数据来检验。

Before collecting data, write down your question and hypothesis. This keeps your investigation focused and shows the examiner you planned ahead.

在收集数据之前，写下你的问题和假设。这能保持调查的焦点，并向考官展示你提前做了规划。

3. Designing Data Collection Methods | 设计数据收集方法

You can gather data in several ways: a survey with a questionnaire, a controlled experiment, systematic observation, or by using secondary sources such as published tables. The method must fit the question. A survey works well for opinions, while an experiment is better for testing cause and effect.

你可以通过多种方式收集数据：使用问卷进行调查、进行对照实验、系统观察或者利用已发布的表格等二手资料。方法必须与问题匹配。问卷适用于收集观点，而实验更适合检验因果关系。

If you design a questionnaire, keep questions simple and unbiased. Instead of “Don’t you agree that sports are the best hobby?”, ask “What is your favourite hobby? (a) Reading (b) Sports (c) Music (d) Other”. This gives clear categories for your frequency table.

如果设计问卷，问题要简单、无偏。不要问“你难道不认为运动是最好的爱好吗？”，而要问“你最喜欢的爱好是什么？(a) 阅读 (b) 运动 (c) 音乐 (d) 其他”。这样能为频率表提供清晰的类别。

For an experiment, identify the independent variable (what you change), the dependent variable (what you measure), and the control variables (what you keep the same). If you are rolling a toy car down a ramp to see how the ramp height affects distance, the height is independent, the distance is dependent, and the surface and car type are controls.

在实验中，要确定自变量（你改变的量）、因变量（你测量的量）和控制变量（你保持不变的量）。如果你将玩具车从斜面上滚下，研究斜面高度如何影响距离，那么高度是自变量，距离是因变量，斜面材质和车型是控制变量。

Record how many data points you plan to collect. A larger sample usually gives more reliable results, but even a class of 25–30 pupils can generate enough data for a meaningful analysis in Year 7.

记录你计划收集多少个数据点。较大的样本量通常会给出更可靠的结果，但即使是一个 25–30 人的班级，也足以为七年级的有意义分析提供数据。

4. Types of Data: Qualitative and Quantitative | 数据类型：定性数据与定量数据

Data fall into two main types. Qualitative (categorical) data describe qualities – eye colour, favourite subject, type of pet. They are usually words or labels. Quantitative data are numbers that can be measured or counted, such as height in centimetres or the number of books read in a month.

数据分为两大类。定性（分类）数据描述品质特征，如眼睛颜色、最喜欢的学科、宠物类型。它们通常是文字或标签。定量数据是可以测量或计数的数字，如以厘米为单位的身高，或一个月内读过的书本数量。

Quantitative data can be further split into discrete and continuous. Discrete data come from counting and take certain values (e.g.

Published by TutorHao | Year 7 统计 Revision Series | aleveler.com

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