📚 Year 11 Eduqas Statistics: Quick Revision Guide to Key Terms | 统计词汇术语速记指南
Mastering statistical terminology is essential for success in the Year 11 Eduqas Statistics exam. This guide pairs every key term with clear, concise definitions and memory tricks, helping you spot the right concept quickly when under time pressure.
掌握统计术语是在 Year 11 Eduqas 统计考试中取得好成绩的关键。本指南将每个重要术语与清晰简洁的定义和记忆诀窍配对，帮助你在时间紧迫时快速识别正确概念。
1. Population vs Sample | 总体与样本
A population is the entire set of individuals or items we wish to study. A sample is a subset drawn from the population to represent it. In exam scenarios, decide whether the data comes from a census (whole population) or a sample survey. Memory shortcut: ‘Population = All, Sample = Some’.
总体是我们希望研究的全体个体或项目。样本是从总体中抽取的一个子集，用以代表总体。考试情境中，需要判断数据来自普查（全体）还是抽样调查。速记窍门：“总体包含所有，样本只取部分”。
2. Parameter and Statistic | 参数与统计量
A parameter is a numerical summary describing a population characteristic, like the population mean μ. A statistic is a numerical summary describing a sample, like the sample mean x̄. Use the first letters to remember: Parameter – Population, Statistic – Sample.
参数是描述总体特征的数值概括，例如总体均值 μ。统计量是描述样本特征的数值概括，例如样本均值 x̄。用首字母记忆：“参数对应总体，统计量对应样本”。
3. Types of Data: Qualitative and Quantitative | 数据类型：定性数据与定量数据
Qualitative (categorical) data record qualities or labels – eye colour, favourite sport – and are not measured numerically. Quantitative data involve numbers and can be counted or measured. Think: ‘Qualitative = Quality, Quantitative = Quantity’. In the Eduqas paper, correctly identifying data type helps choose the right chart.
定性（分类）数据记录的是性质或标签——眼睛颜色、最喜欢的运动——不是用数字衡量的。定量数据涉及数字，可以计数或测量。记忆法：“定性关注质量，定量关注数量”。Eduqas 试卷中，正确识别数据类型有助于选择合适的图表。
4. Discrete and Continuous Data | 离散数据与连续数据
Discrete data can only take specific, separate values – often integers, like the number of cars in a car park. Continuous data can take any value within an interval, including decimals – like the mass of a chocolate bar. A quick way to distinguish: discrete data are counted, continuous data are measured.
离散数据只能取特定的、分离的值——通常是整数，如停车场里汽车的数量。连续数据可以在一个区间内取任意值，包括小数——如一块巧克力的质量。快速区分方法：离散数据可数，连续数据可量。
5. Measures of Central Tendency | 集中趋势的度量
Mean, median and mode are the three common averages. Mean = sum / number of values; sensitive to outliers. Median = middle value when ordered; robust to extremes. Mode = most frequent value; useful for non-numerical data. Memory hook: ‘Mean – average Joe, Median – middleman, Mode – most popular.’ Practice calculating them from frequency tables and grouped data, as required by Eduqas.
均值、中位数和众数是三种常用平均数。均值 = 总和 / 数值个数，易受异常值影响。中位数 = 排序后的中间值，抗极端值能力强。众数 = 出现次数最多的值，适用于非数值数据。记忆钩子：“均值像平均先生，中位数是中间人，众数是人气王”。根据 Eduqas 要求，需练习从频数表和分组数据中计算它们。
6. Measures of Spread: Range and IQR | 离散程度的度量：极差与四分位距
Range = highest value – lowest value. It gives the full spread but is affected by outliers. Interquartile range (IQR) = upper quartile – lower quartile, covering the middle 50% of data; it is resistant to outliers. Think: ‘Range is the whole stretch, IQR is the central chunk.’ Be able to find quartiles and construct box plots for Eduqas questions.
极差 = 最大值 – 最小值。它给出全距但易受异常值影响。四分位距 (IQR) = 上四分位数 – 下四分位数，涵盖中间 50% 的数据，对异常值稳健。记忆：“极差是全跨度，IQR 是中间块”。要能够为 Eduqas 考题求四分位数并绘制箱线图。
7. Standard Deviation and Variance | 标准差与方差
Variance measures how far a set of numbers is spread out: the average of the squared differences from the mean. Standard deviation is the square root of variance, expressed in the original units. Both quantify dispersion; the larger the value, the more spread out the data. Memory trick: ‘Square the deviations, average them (variance), then take the square root (standard deviation).’ Exam papers often supply the formula; you just need to substitute correctly.
方差衡量一组数值的分散程度：与均值之差的平方的平均值。标准差是方差的算术平方根，以原始单位表示。两者都量化离散程度；值越大，数据越分散。速记诀窍：“先差平方求平均得方差，再开方得标准差”。考试常提供公式，只需正确代入。
8. Data Presentation: Charts and Graphs | 数据展示：图表
Bar charts – for categorical data, bars have gaps. Pie charts – show proportions of a whole. Histograms – for continuous data, area ∝ frequency, bars touch. Frequency polygons – join midpoints of histogram bars. Cumulative frequency curves – show running totals, used to estimate medians and quartiles. Eduqas spotlight: check axis labels and scales, and use a ruler for straight lines.
条形图——用于分类数据，长条间有间隙。饼图——显示整体中各部分的比例。直方图——用于连续数据，面积代表频数，长条之间无间隙。频数多边形——连接直方图中各条形中点。累积频数曲线——展示累计总数，用于估计中位数和四分位数。Eduqas 要点：检查轴标签和刻度，画直线用直尺。
9. Scatter Graphs and Correlation | 散点图与相关性
A scatter graph displays the relationship between two variables. Positive correlation: y tends to increase as x increases. Negative correlation: y tends to decrease as x increases. No correlation: no clear pattern. The line of best fit can be drawn by eye and used for interpolation or extrapolation. Remember: correlation does not imply causation. Outliers can pull the line of best fit, so treat them cautiously.
散点图显示两个变量之间的关系。正相关：随着 x 增大，y 也趋于增大。负相关：随着 x 增大，y 趋于减小。无相关性：没有明显模式。可以凭借目测画出最佳拟合线，用于内插或外推。记住：相关不代表因果。异常值可能拉动最佳拟合线，需谨慎处理。
10. Probability Terminology | 概率术语
Probability = number of favourable outcomes / total number of outcomes. It ranges from 0 (impossible) to 1 (certain). Mutually exclusive events cannot occur simultaneously. Independent events have no influence on each other’s probabilities. Conditional probability is written as P(A|B), read ‘probability of A given B’. For Eduqas, expect tree diagrams and Venn diagrams, and know how to fill probabilities correctly.
概率 = 有利结果数 / 总结果数。取值范围从 0（不可能）到 1（必然）。互斥事件不能同时发生。独立事件彼此不影响概率。条件概率记作 P(A|B)，读作“在 B 发生的条件下 A 的概率”。Eduqas 考试会涉及树状图和文氏图，要会正确填写概率。
11. Statistical Hypothesis Testing | 统计假设检验
A hypothesis test uses sample data to assess a claim about a population parameter. The null hypothesis (H₀) represents the status quo (no effect, no change). The alternative hypothesis (H₁) challenges H₀. A significance level, often 5%, defines the risk we are willing to take of rejecting H₀ when it is true. If the test statistic falls into the critical region (or p-value < significance level), reject H₀. Eduqas may include simple one‑tailed and two‑tailed tests.
假设检验使用样本数据来评估关于总体参数的某种说法。原假设 (H₀) 表示现状（无效果、无差异）。备择假设 (H₁) 质疑原假设。显著性水平，通常为 5%，定义了拒绝真实的 H₀ 所愿承担的风险。如果检验统计量落入临界区域（或 p 值 < 显著性水平），则拒绝 H₀。Eduqas 可能包括简单的单尾和双尾检验。
Published by TutorHao | Statistics Revision Series | aleveler.com
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