📚 Constructing a Hypothesis in A-Level Biology | A-Level 生物中构建假设
Constructing a hypothesis is one of the most important skills in A-Level Biology practical work and experimental design. A well-written hypothesis turns a broad question into a specific, testable statement that links an independent variable to a dependent variable. This article explains how to build, use and evaluate hypotheses in the Cambridge A-Level Biology syllabus.
构建假设是 A-Level 生物实验操作与实验设计中最重要的技能之一。一个写得好的假设能把宽泛的问题转化为具体的、可检验的陈述，将自变量与因变量联系起来。本文将解释如何在剑桥 A-Level 生物课程中构建、使用和评价假设。
1. What Is a Hypothesis? | 什么是假设？
A hypothesis is a clear, testable statement that predicts the relationship between two or more variables. It is not a random guess or a question: it must be based on existing scientific knowledge and be written in a way that can be supported or refuted by experimental data. In biology, hypotheses often propose a causal link between a factor you change and a response you measure.
假设是一种清晰、可检验的陈述，用于预测两个或多个变量之间的关系。它不是随意猜测，也不是问题：假设必须基于已有的科学知识，并且写作方式要能够被实验数据支持或否定。在生物学中，假设通常提出你所改变的因素与你所测量的反应之间的因果联系。
For example, after observing that pondweed produces more bubbles in bright light, a student might construct the hypothesis: “Increasing light intensity increases the rate of photosynthesis in Elodea.” This statement can be tested by measuring oxygen production at different light intensities.
例如，学生观察到强光下水蕴草产生更多气泡后，可以构建这样的假设：”增加光照强度会提高伊乐藻的光合作用速率。” 这一陈述可以通过在不同光照强度下测量氧气产量来检验。
2. Hypothesis vs Prediction | 假设与预测的区别
A hypothesis and a prediction are often confused, but they are not the same. A hypothesis is a proposed explanation for a relationship; it is more general and includes reasoning. A prediction is a specific statement of what should happen under particular conditions if the hypothesis is correct. Predictions are usually framed with “if…then…” logic.
假设和预测经常被混淆，但它们并不相同。假设是对一种关系提出的解释，它更概括，并包含推理。预测则是在特定条件下，如果假设正确，应该发生什么的具体陈述。预测通常使用 “如果……那么……” 的逻辑来表述。
Consider the hypothesis: “Enzyme activity increases with temperature up to an optimum because molecules gain kinetic energy.” From this, a prediction would be: “If amylase is tested at 40 °C, then the time taken to break down starch will be shorter than at 20 °C.” The hypothesis explains why, while the prediction states what will be observed.
以这个假设为例：”由于分子获得动能，酶活性随温度升高而增加，直至达到最适温度。” 由此产生的预测是：”如果在 40 °C 下测试淀粉酶，那么分解淀粉所需时间将比在 20 °C 下更短。” 假设解释原因，而预测说明将观察到什么。
3. The Role of Variables | 变量的作用
A well-formed hypothesis must identify the independent variable (IV) and the dependent variable (DV). The independent variable is the factor you deliberately change or manipulate. The dependent variable is the factor you measure or observe. Controlled variables are all other factors that must be kept constant to ensure a fair test.
一个构造良好的假设必须明确自变量和因变量。自变量是你有意改变或操纵的因素。因变量是你测量或观察的因素。控制变量是所有其他必须保持不变的因素，以确保实验公平。
For instance, in the hypothesis “As light intensity increases, the rate of photosynthesis increases,” light intensity is the IV and the rate of photosynthesis is the DV. Controlled variables might include temperature, carbon dioxide concentration, plant species and the length of the experiment. A hypothesis that does not mention these variables is usually too vague for A-Level work.
例如，在 “随着光照强度增加，光合作用速率提高” 这一假设中，光照强度是自变量，光合作用速率是因变量。控制变量可能包括温度、二氧化碳浓度、植物种类和实验时长。一个没有提到这些变量的假设通常对 A-Level 学习来说过于模糊。
4. Types of Hypothesis: Null and Alternative | 假设类型：零假设与备择假设
In many A-Level Biology investigations, especially those involving statistical tests, you must write two formal hypotheses. The null hypothesis (H₀) states that there is no significant difference or relationship between variables. The alternative hypothesis (H₁) states that there is a significant difference or relationship.
在许多 A-Level 生物探究中，尤其是涉及统计检验的实验，你必须写出两个正式假设。零假设 (H₀) 声明变量之间没有显著差异或关系。备择假设 (H₁) 声明变量之间存在显著差异或关系。
For a chi-squared test in genetics, H₀ might be “There is no significant difference between the observed phenotypic ratio and the expected 9:3:3:1 ratio.” H₁ would be “There is a significant difference between the observed and expected ratios.” Statistical tests then decide whether to reject H₀.
对于遗传学中的卡方检验，H₀ 可能是 “观测到的表型比例与预期的 9:3:3:1 比例之间没有显著差异。” H₁ 则是 “观测到的比例与预期比例之间存在显著差异。” 统计检验随后决定是否拒绝 H₀。
5. Writing a Testable Hypothesis | 写出可检验的假设
A testable hypothesis should be specific, measurable, falsifiable and directional where possible. It should name the variables, state how the dependent variable will change and give a brief scientific reason. A useful template is: “As [IV] increases/decreases, [DV] increases/decreases because [scientific reasoning].”
一个可检验的假设应当具体、可测量、可证伪，并尽可能有方向性。它应指出变量，说明因变量如何变化，并给出简要的科学理由。一个有用的模板是：”随着 [自变量] 增加/减少，[因变量] 增加/减少，因为 [科学推理]。”
Bad hypothesis: “Light affects plants.” This is too vague because it does not say how light affects plants or what will be measured. Improved hypothesis: “Increasing light intensity from 200 lux to 1000 lux increases the rate of photosynthesis in Elodea, measured as oxygen production per minute, because more light energy is available for the light-dependent reactions.”
不好的假设：”光照影响植物。” 这个说法太模糊，因为没有说明光照如何影响植物，也没有说明要测量什么。改进后的假设：”将光照强度从 200 勒克斯增加到 1000 勒克斯，会提高伊乐藻的光合作用速率（以每分钟氧气产量衡量），因为光反应可获得更多光能。”
6. Operationalising Variables | 变量的可操作化
Operationalisation means defining exactly how a variable will be measured or controlled. This makes the hypothesis testable and allows other scientists to replicate the investigation. Without operational definitions, results cannot be interpreted consistently.
可操作化是指确切定义如何测量或控制变量。这使得假设可检验，并允许其他科学家重复该探究。如果没有可操作的定义，结果就无法得到一致的解释。
For enzyme activity, “activity” is not directly measurable. An operationalised DV might be “the volume of oxygen gas produced per minute, measured in cm³ min⁻¹ using a gas syringe.” For seed germination, a biologist might define germination as “the percentage of seeds with a radicle length greater than 2 mm after 48 hours.”
以酶活性为例，”活性” 无法直接测量。可操作化的因变量可以是 “每分钟产生的氧气体积，使用气体注射器测量，单位为 cm³ min⁻¹。” 对于种子萌发，生物学家可能将萌发定义为 “48 小时后胚根长度大于 2 mm 的种子所占百分比。”
7. Directional and Non-directional Hypotheses | 方向性与非方向性假设
A directional hypothesis states the expected direction of the effect, such as “increases,” “decreases,” “higher than” or “lower than.” It is used when previous evidence or biological theory clearly suggests which way the result should go. A non-directional hypothesis simply states that there will be a difference or relationship, without specifying the direction.
方向性假设说明预期的效应方向，例如 “增加”、”减少”、”高于” 或 “低于”。当先前的证据或生物学理论清楚表明结果应该朝哪个方向发展时，使用方向性假设。非方向性假设只说明会存在差异或关系
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