Key Points for SQA Statistics Experimental / Practical Assessment | SQA统计实验/实践考核要点

📚 Key Points for SQA Statistics Experimental / Practical Assessment | SQA统计实验/实践考核要点

This article outlines the essential practical assessment skills for Year 9 SQA Statistics. Understanding experimental design, data handling, and evaluation is critical for success in the practical examination. We will cover key topics from planning an experiment to interpreting results and identifying sources of error.

本文概述了九年级SQA统计课程中实验与实践考核的核心要点。掌握实验设计、数据处理和评估方法对于在实践考试中取得成功至关重要。我们将涵盖从规划实验到解释结果、识别误差来源等关键主题。

1. Principles of Experimental Design | 实验设计原则

Every statistical investigation starts with a clear, testable question. Define the independent variable (the one you change), the dependent variable (the one you measure), and all control variables that must be kept constant to ensure a fair test. A well-designed experiment includes randomisation to reduce bias and replication to improve reliability.

每一项统计调查都始于一个清晰、可检验的问题。明确自变量（你改变的变量）、因变量（你测量的变量）以及所有必须保持不变的控制变量，以确保实验的公平性。一个精心设计的实验应包含随机化以减少偏差，并通过重复实验来提高可靠性。

The sample size must be large enough to show patterns and reduce the effect of random variation. In practical assessments, you may need to justify why you chose a particular sample size. Always consider practical limitations, such as time and resources.

样本量必须足够大，才能显示出模式并减少随机变异的影响。在实践考核中，你可能需要说明为什么选择某个特定的样本量。同时要始终考虑时间和资源等实际限制。

Before collecting data, write a clear plan that includes the hypothesis, method, and data recording sheets. This helps you stay organised and ensures no data is missed during the experiment.

在收集数据之前，制定一份清晰的计划，包括假设、方法和数据记录表。这有助于你保持条理，并确保实验过程中不会遗漏任何数据。

2. Data Collection and Sampling Methods | 数据收集与抽样方法

Data can be collected through experiments, surveys, or observational studies. The method must match the research question. For example, if investigating plant growth under different light conditions, controlled experiments with precise measurements are appropriate. Surveys might use questionnaires to gather opinions or counts.

数据可以通过实验、调查或观察研究来收集。方法必须与研究问题相匹配。例如，调查不同光照条件下植物生长的情况，适合采用精确测量的对照实验。而调查问卷则可用于收集意见或计数。

When you cannot measure the entire population, use a sampling method. Simple random sampling gives each member an equal chance of selection. Stratified sampling divides the population into groups and samples proportionally. Systematic sampling selects every kth individual. Convenience sampling is easy but often biased; avoid it if possible.

当你无法测量整个总体时，使用抽样方法。简单随机抽样使每个成员被选中的机会均等。分层抽样将总体分组，并按比例抽样。系统抽样每隔一定数量选取一个个体。便利抽样虽然简单，但往往存在偏差，应尽量避免。

In a practical task, you might need to describe how you selected your sample. For a fair test, random allocation of subjects to treatment groups is crucial. Always watch out for selection bias and non‑response bias.

在实践任务中，你可能需要描述是如何选取样本的。为了进行公平测试，将受试者随机分配到处理组至关重要。始终要警惕选择偏差和无回应偏差。

3. Types of Data and Measurement Scales | 数据类型与测量尺度

Data types are crucial for choosing the right analysis and chart. Qualitative (categorical) data can be nominal (e.g., eye colours: blue, brown) or ordinal (e.g., satisfaction ratings: low, medium, high). Quantitative (numerical) data can be discrete (countable, like number of students) or continuous (measurable, like height).

数据类型对于选择正确的分析方法和图表至关重要。定性（分类）数据可分为名义数据（如眼睛颜色：蓝色、棕色）和有序数据（如满意度评级：低、中、高）。定量（数值）数据可分为离散数据（可数的，如学生人数）和连续数据（可测量的，如身高）。

The table below summarises the main data types with examples to help you identify them during an experiment.

下表总结了主要的数据类型及示例，以帮助你在实验中识别它们。