📚 KS3 CCEA Statistics: Terminology Quick-Memorisation Guide | KS3 CCEA 统计：词汇术语速记指南
Welcome to your KS3 CCEA statistics terminology cheat sheet! Building a strong foundation in the language of data will help you understand concepts, interpret graphs, and solve problems with confidence. This guide pairs key terms with clear explanations in both English and Chinese, so you can master the vocabulary faster.
欢迎来到你的 KS3 CCEA 统计术语速记指南！打牢数据语言的基础，能帮助你轻松理解概念、解读图表并自信地解决问题。本指南为每个关键术语配上了中英双语清晰的解释，助你快速掌握统计词汇。
1. Data and Variables | 数据与变量
Data are facts, numbers, or observations collected for analysis. A variable is any characteristic that can take different values, such as height, shoe size, or favourite colour.
数据是收集起来用于分析的事实、数字或观察结果。变量是任何可以取不同值的特征，比如身高、鞋码或最喜欢的颜色。
Variables can be qualitative (categorical) — describing qualities like hair colour or car type — or quantitative (numerical) — measured with numbers like mass or temperature.
变量可以是定性的（分类变量）——描述头发颜色或汽车类型等性质；也可以是定量的（数值变量）——用数字测量的，如质量或温度。
2. Mean | 平均数
The mean is the average you get by adding up all the values and dividing by how many values there are. It is the most common measure of central tendency.
平均数是把所有数值加起来，再除以数值的个数所得的结果。它是最常用的集中趋势度量。
Mean = ∑ (all values) ÷ number of values
平均数 = 所有数值之和 ÷ 数值的个数
Be careful — the mean can be pulled up or down by extremely high or low values (outliers).
要小心——平均数可能会被极高或极低的值（异常值）拉高或拉低。
3. Median | 中位数
The median is the middle number when you put all values in order from smallest to largest. If there are two middle numbers, the median is the mean of those two.
中位数是将所有数值从小到大排列后，位于中间的那个数。如果有两个中间的数，中位数就是这两个数的平均数。
The median is not affected by outliers, so it often gives a better idea of a ‘typical’ value for skewed data, like house prices or incomes.
中位数不受异常值影响，因此在数据偏斜时（如房价或收入），通常能更好地反映“典型”值。
4. Mode | 众数
The mode is the value that appears most often in a data set. A set can have one mode, more than one mode (bimodal or multimodal), or no mode at all if all values occur only once.
众数是数据集中出现次数最多的数值。一组数据可以有一个众数、多个众数（双峰或多峰），如果所有值只出现一次，则没有众数。
The mode is the only measure of average that can be used with qualitative data — for example, the most popular car colour in a survey.
众数是唯一可用于定性数据的平均数度量——例如，调查中最受欢迎的汽车颜色。
5. Range | 范围（极差）
Range measures how spread out the data are. It is simply the difference between the largest value and the smallest value.
范围衡量数据的分散程度。它就是最大值与最小值的差。
Range = Largest value − Smallest value
范围 = 最大值 − 最小值
A small range means the data are tightly clustered; a large range suggests a wide spread. Remember, the range is easily affected by outliers.
范围小说明数据密集；范围大说明数据分散。记住，范围很容易受异常值影响。
6. Frequency and Frequency Tables | 频数与频数表
Frequency is the number of times a particular value or category appears in a data set. A frequency table organises data by listing each item and how often it occurs, often with a tally column to help counting.
频数是某个特定值或类别在数据集中出现的次数。频数表通过列出每个项目及其出现次数来整理数据，通常带有一列画记符号帮助计数。
Frequency tables can be used for both discrete data (e.g. number of pets) and grouped continuous data (e.g. heights organised into intervals).
频数表既可用于离散数据（如宠物数量），也可用于分组连续数据（如将身高分组为区间）。
| Colour / 颜色 | Tally / 画记 | Frequency / 频数 |
|---|---|---|
| Red | |||| | 4 |
| Blue | || | 2 |
7. Charts and Graphs | 统计图表
Charts turn numbers into pictures, making patterns easier to see. Common types at KS3 include bar charts, pictograms, pie charts, line graphs and scatter graphs.
图表将数字转化为图像，使模式一目了然。KS3 阶段常见的图表有：条形图、象形图、饼图、折线图和散点图。
Bar charts display categorical data with rectangular bars whose lengths represent frequency. In pictograms, each picture stands for a certain number of items. Pie charts show proportions as slices of a circle. Line graphs join data points to show trends over time. Scatter graphs plot two variables against each other to look for relationships.
条形图用长方形条展示分类数据，条的长度代表频数。象形图中每个图标代表一定数量的项目。饼图用扇形表示比例。折线图连接数据点展示随时间变化的趋势。散点图将两个变量对应描点，用于寻找关系。
8. Correlation | 相关性
Correlation describes the relationship between two variables on a scatter graph. If points slope upwards, the correlation is positive; if they slope downwards, it is negative. No clear pattern means little or no correlation.
相关性描述散点图上两个变量之间的关系。如果点群向上倾斜，是正相关；向下倾斜则是负相关。没有明显模式说明相关性很弱或没有。
Correlation does not imply causation — just because ice cream sales and sunglasses sales both rise in summer, one does not cause the other.
相关不代表因果——仅仅因为夏季冰淇淋销量和太阳镜销量同时上升，并不意味着一方导致另一方。
A line of best fit (trend line) can be drawn through the points to help make predictions, but be careful when extrapolating beyond the data.
可以通过数据点画出最佳拟合线（趋势线）帮助预测，但在数据范围之外进行外推时要小心。
9. Outliers | 异常值
An outlier is a data point that lies far away from the rest of the values in a set. Outliers can be caused by measurement errors or genuine rare events.
异常值是数据集中远离其他数值的数据点。异常值可能源于测量误差，也可能是真实的罕见事件。
Outliers can strongly affect the mean and range, so it is important to identify them and decide whether to include or exclude them in calculations based on context.
异常值可能强烈影响平均数和范围，因此识别并根据实际情况决定在计算时保留还是剔除它们非常重要。
10. Basic Probability Language | 概率基础术语
Probability measures how likely an event is to happen, expressed as a number between 0 (impossible) and 1 (certain), or as a fraction, decimal or percentage.
概率衡量事件发生的可能性，用 0（不可能）到 1（必然）之间的数字表示，也可以用分数、小数或百分比表示。
Probability = number of favourable outcomes ÷ total number of possible outcomes
概率 = 有利结果数 ÷ 所有可能结果的总数
Key terms: an experiment is a repeatable process with observable results. An outcome is a possible result. An event is a set of one or more outcomes. The sample space is the list of all possible outcomes.
关键术语：试验是一个可重复、有可观察结果的过程。结果是可能发生的结果。事件是一个或多个结果的集合。样本空间是所有可能结果的列表。
11. Discrete vs Continuous Data | 离散数据与连续数据
Discrete data can only take certain values, usually whole numbers — for example, the number of students in a class (25, 26, 27…). You cannot have 25.7 students.
离散数据只能取特定的值，通常是整数——例如，班级学生人数（25, 26, 27 …）。你不能有 25.7 个学生。
Continuous data can take any value within a range and is measured, not counted — height, mass, time. These values are often grouped into intervals for analysis.
连续数据可以在一个范围内取任何值，是测量得到而非计数的——如身高、质量、时间。这些值通常被分组到区间中进行分析。
12. Primary and Secondary Data | 一手数据与二手数据
Primary data is information you collect yourself for a specific purpose, for example by conducting a survey or experiment. It is up-to-date and tailored to your question but can be time-consuming to gather.
一手数据是你自己为特定目的收集的信息，例如通过调查或实验获得。它是最新的、贴合你的问题，但收集起来可能耗时。
Secondary data is data that someone else has already collected, such as statistics from websites, books or government reports. It is quicker to obtain but you must check its reliability and relevance.
二手数据是别人已经收集好的数据，比如来自网站、书籍或政府报告的统计资料。获取更快，但你必须检查其可靠性和相关性。
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