📚 Year 10 CIE Statistics: Key Terms Memory Guide | 10年级 CIE 统计词汇速记指南
In CIE IGCSE Statistics, a solid grip on terminology can transform confusion into clarity. This quick-reference guide breaks down every essential term you need for Year 10, pairing crisp definitions with memorable triggers so you can recall them effortlessly in class and in the exam.
在 CIE IGCSE 统计中，牢牢掌握术语能让混沌变得清晰。这份速记指南将 10 年级所需的核心术语逐一拆解，用简洁定义搭配生动的记忆触发器，帮助你在课堂和考试中轻松调取知识。
1. Types of Data | 数据类型
Qualitative data (categorical) are labels or descriptions. They answer the question “what kind” and cannot be measured on a numerical scale. Examples include hair colour, favourite cuisine, or survey responses like “yes” and “no”.
定性数据（分类数据）是标签或描述，回答“哪一种”，不能以数值尺度衡量。例如头发颜色、最喜欢的菜系或“是/否”型问卷答案。
Quantitative data are numbers obtained by counting or measuring. They split into two subtypes: discrete and continuous.
定量数据是通过计数或测量得到的数字，分为离散型和连续型两个子类。
Discrete data take only exact, separate values—usually whole numbers. Think of things you can count: number of pets, shoe size, goals scored in a match.
离散数据只能取精确的、独立的值，通常是整数。想想那些你能数出来的事物：宠物数量、鞋码、比赛进球数。
Continuous data can take any value within a range and are measured, not counted. Height, mass, temperature, and time are continuous. You can always imagine a finer measurement between two values.
连续数据在一个范围内可以取任意值，是测量而非计数得到的。身高、质量、温度和时间都是连续的。你总能在两个值之间想象出更精细的刻度。
Ordinal data are a special kind of qualitative data that come with a natural order but no fixed numerical difference between ranks. Satisfaction ratings (poor, average, good) and competition medals are ordinal.
顺序数据是一种特殊的定性数据，具有天然的顺序，但等级之间没有固定的数值差。满意度评分（差、一般、好）和比赛奖牌都属于顺序数据。
Quick memory chain: Qualitative = Quality, Quantitative = Quantity. Discrete = Digits (countable), Continuous = Curve (measurable). Ordinal has Order but no equal gaps.
快速记忆链：Qualitative 联想 Quality（品质），Quantitative 联想 Quantity（数量）。Discrete 联想 Digits（可数数字），Continuous 联想 Curve（曲线，可测）。Ordinal 有 Order（顺序）但没有等距间隔。
2. Data Collection Methods | 数据收集方法
Primary data are gathered directly by the researcher for the investigation at hand. Conducting a classroom survey, measuring plant growth in a lab, or recording your own reaction times are all examples of primary data collection.
原始数据是研究者为当前调查直接收集的。在班级做问卷调查、在实验室测量植物生长或记录自己的反应时间，都是收集原始数据的例子。
Secondary data have been collected by someone else for a different purpose. You might use a government census, historical weather logs, or statistics published in a newspaper. It saves time but may not perfectly suit your question.
二手数据是他人因其他目的已经收集好的数据。你可能会用到政府人口普查、历史气象记录或报纸上发布的统计。它省时，但未必完全贴合你的问题。
A census collects information from every member of a population. A sample takes a subset. A census is accurate but often impractical; a sample is quicker and cheaper but must be chosen carefully to avoid bias.
普查从总体的每个成员处收集信息。样本只取一个子集。普查准确但常不切实际；样本更快捷、便宜，但必须谨慎选取以避免偏差。
Memory hook: Primary = Personal (firsthand), Secondary = Second-hand (passed on), Census = Complete count, Sample = Snippet.
记忆挂钩：Primary 联想 Personal（亲力亲为），Secondary 联想 Second-hand（二手的），Census 等于 Complete count（全盘计数），Sample 是 Snippet（一小片）。
3. Sampling Techniques | 抽样技术
Simple random sampling gives every member of the population an equal chance of selection, like drawing names from a hat. It is fair but needs a full list of the population.
简单随机抽样让总体中每个成员被选中的机会均等，如从帽子里抽名字。它公平，但需要完整的人口名单。
Stratified sampling splits the population into distinct groups (strata) and then randomly samples from each in proportion to its size. This ensures subgroups are fairly represented.
分层抽样将总体按特征分成不同的组（层），然后按各组所占比例随机抽样，确保子群体被充分代表。
Systematic sampling selects individuals at regular intervals from a list, e.g. every 10th name. It is simple but can introduce bias if the list has a hidden pattern.
等距抽样从名单中每隔固定间隔选取个体，如每第10个名字。它简单易行，但如果名单存在隐藏规律则可能引入偏差。
Quota sampling and convenience sampling are non-probability methods. Quota sampling aims to fill fixed numbers of various groups; convenience sampling picks whoever is easy to reach. Both are prone to bias.
配额抽样和便利抽样都是非概率方法。配额抽样旨在填补各组固定人数；便利抽样则选择最容易接触到的对象。两者都容易产生偏差。
Memory picture: Stratified = Strata (layers) represented proportionally. Systematic = Step-by-step interval. Quota = Target quotas you must fill.
记忆画像：Stratified 可想象地层（strata）按比例取样；Systematic 是每隔固定步长取样；Quota 是必须填满的目标配额。
4. Frequency Distributions | 频率分布
When data are grouped, we define class intervals (e.g. 10-19, 20-29). The class boundaries are the true limits of the interval: for continuous data the interval 10-19 has boundaries 9.5 and 19.5, ensuring no gaps.
数据分组时我们需要定义组距（如10-19、20-29）。组界是区间的真实界限：对于连续数据，10-19的组界为9.5和19.5，确保没有空隙。
The class width is the difference between the upper and lower boundaries. The midpoint is (lower boundary + upper boundary) / 2 and is used in calculations of the mean from grouped data.
组宽是上组界与下组界之差。组中值等于(下组界+上组界)/2，用于分组数据均值的计算。
Cumulative frequency is the running total of frequencies. It helps construct cumulative frequency curves and find quartiles. Always check that the final cumulative total equals the total number of observations.
累积频率是频率的累计和，用于绘制累积频率曲线以及求四分位数。务必检查最后的累积总数是否等于观测总次数。
Table to lock in terms:
| Term | Meaning | 术语 | 含义 |
|---|---|---|---|
| Class boundaries | Real limits of the interval | 组界 | 区间的真实界限 |
| Midpoint | Centre of the class interval | 组中值 | 组距的中心点 |
| Cumulative frequency | Sum of frequencies up to that class | 累积频率 | 截至该组的频率之和 |
Memory motto: Boundaries are the ‘Borders’ where data truly live. Cumulative starts from the ground and climbs.
记忆口诀：组界是数据真实居住的“边界”。累积频率从零开始，逐级爬升。
5. Graphical Representations | 统计图表
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