📚 Planning an Investigation in A-Level Biology | A-Level 生物实验调查的规划
Planning is a core skill in Cambridge International AS and A Level Biology. A well-planned investigation allows you to test a hypothesis reliably, collect valid data and draw justified conclusions. This guide breaks the planning process into key stages that are regularly examined in Paper 5 and practical assessments.
规划是剑桥国际 AS 和 A Level 生物学科的一项核心技能。一份设计周密的实验调查使你能够可靠地检验假设、收集有效数据并得出有依据的结论。本指南将规划过程分解为多个关键阶段，这些阶段在 Paper 5 和实验评估中经常考查。
1. The Purpose of Planning | 规划的目的
Planning turns a general question into a workable experiment. It forces you to identify variables, choose equipment, decide sample sizes and predict outcomes before practical work begins. Without planning, an investigation may produce data that cannot answer the original question.
规划将一个一般性问题转化为可操作的实验。它迫使你在实验开始前确定变量、选择设备、决定样本量并预测结果。如果没有规划，实验调查可能产生无法回答原始问题的数据。
Good planning also saves time and reduces waste, because each reagent, measurement and repeat has a clear role. It allows you to anticipate problems such as a limited supply of biological material or a measurement that is difficult to read precisely.
良好的规划还能节省时间并减少浪费，因为每种试剂、每次测量和重复实验都有明确的作用。它使你能预见问题，例如生物材料供应有限或测量难以精确读数。
2. Formulating a Testable Hypothesis | 提出可检验的假设
A hypothesis is a specific, testable statement predicting the effect of the independent variable on the dependent variable. For example: “Increasing substrate concentration increases the initial rate of an enzyme-catalysed reaction until the active sites become saturated.” This states a clear relationship and a predicted direction.
假设是一个具体、可检验的陈述，预测自变量对因变量的影响。例如：“增加底物浓度会提高酶催化反应的初始速率，直到活性位点饱和。” 这一表述同时给出了明确的关系和预测方向。
The null hypothesis states there is no significant effect or difference. For the example above it would be: “Substrate concentration has no significant effect on the initial rate of reaction.” Statistical tests are used to decide whether the data allow the null hypothesis to be rejected.
零假设指出没有显著影响或差异。对于上述例子，零假设为：“底物浓度对初始反应速率没有显著影响。” 使用统计检验来判断数据是否允许拒绝零假设。
Avoid vague aims such as “to investigate temperature” because they do not state a predicted relationship or allow a clear experimental design. A precise hypothesis guides the choice of range, interval and controls.
避免模糊的目标，如“研究温度”，因为它们没有说明预测的关系，也无法指导清晰的实验设计。精确的假设可指导范围、间隔和对照组的选择。
3. Independent and Dependent Variables | 自变量与因变量
The independent variable is the factor you change or set. The dependent variable is the factor you measure. All other variables must be controlled so they do not affect the dependent variable. The table below gives typical examples.
自变量是你改变或设定的因素。因变量是你测量的因素。所有其他变量必须加以控制，使其不影响因变量。下表给出典型示例。
| Investigation | Independent variable | Dependent variable |
| Osmosis in potato strips | Concentration of sucrose solution | Percentage change in mass |
| Beetroot membrane permeability | Temperature of water bath | Absorbance of pigment released |
| Enzyme activity | Substrate concentration | Initial rate of oxygen production |
In each case, the independent variable must be set precisely using serial dilutions or thermostatically controlled water baths. The dependent variable should be quantitative where possible so that means and standard deviations can be calculated.
在每种情况下，自变量都必须通过梯度稀释或恒温水浴精确设定。因变量应尽可能量化，以便计算平均值和标准差。
4. Controlling Confounding Variables | 控制混杂变量
Confounding variables are factors that could also affect the dependent variable. They must be kept constant or monitored so they do not produce a false trend. In an enzyme reaction, pH, enzyme concentration and temperature are common confounding variables.
混杂变量是也可能影响因变量的因素。必须保持恒定或监测，以免产生虚假趋势。在酶促反应中，pH、酶浓度和温度是常见的混杂变量。
Use a buffer solution to maintain pH and a water bath to maintain temperature. If the substrate volume is changed, keep the total volume constant for each tube so that enzyme concentration remains the same.
使用缓冲液保持 pH，使用水浴保持温度。如果改变底物体积，应保持每管总体积不变，使酶浓度保持一致。
It is impossible to control every variable, so record any that could not be held constant and treat them as potential limitations. This is better than ignoring them, because it demonstrates awareness of experimental error.
不可能控制所有变量，因此要记录任何无法保持恒定的变量，并将其视为潜在局限。这比忽略它们更好，因为能体现对实验误差的认识。
5. Choosing an Appropriate Experimental Design | 选择合适的实验设计
A valid design allows a clear comparison between conditions. Common designs include a range of independent variable values, a control group and matched samples. For example, a serial dilution of sucrose solutions gives five or six different concentrations for potato cylinders.
有效的设计允许在不同条件之间进行清晰比较。常见设计包括一系列自变量值、一个对照组和匹配样本。例如，蔗糖溶液的梯度稀释为土豆圆柱提供五个或六个不同浓度。
Decide the range and intervals of the independent variable carefully. The range must cover the expected response, and intervals must be small enough to reveal the shape of the relationship. A very wide interval may miss a sharp change, while a very narrow range may not show a full trend.
认真确定自变量的范围和间隔。范围必须覆盖预期响应，间隔必须足够小以揭示关系的形状。过大的间隔可能错过急剧变化，而过窄的范围可能无法显示完整趋势。
If living organisms are used, select specimens of similar age, size, mass and health to reduce biological variation. Random sampling or random allocation helps prevent bias in assigning specimens to treatments.
如果使用活体生物，应选择年龄、大小、质量和健康状况相似的个体，以减少生物学差异。随机抽样或随机分配有助于防止在分配个体到不同处理时产生偏差。
6. Controls and Baseline Measurements | 对照组与基线测量
A control group or control treatment receives no intervention or a standard condition. It provides a baseline to show that the measured effect is due to the independent variable and not to another factor. For example, when testing an inhibitor, the control contains the enzyme and substrate but no inhibitor.
对照组或对照处理不接受干预或处于标准条件。它提供基线，表明测得的效果是由自变量引起的，而不是由其他因素引起的。例如，在测试抑制剂时，对照组含有酶和底物但不含抑制剂。
In an osmosis experiment, distilled water acts as a reference because it has zero solute concentration and therefore has a defined water potential. A negative control can also show what happens when no active ingredient is present.
在渗透作用实验中，蒸馏水可作为参考，因为其溶质浓度为零，因而具有确定的水势。阴性对照还可显示没有活性成分时会发生什么。
A baseline measurement is equally important before applying a treatment, such as measuring the initial mass or initial absorbance of each sample. This allows you to calculate the change caused by the treatment rather than relying only on final values.
在施加处理前进行基线测量同样重要，例如测量每个样品的初始质量或初始吸光度。这样你就可以计算处理引起的变化，而不是只依赖最终值。
7. Repeats, Replicates and Randomisation | 重复、平行样与随机化
Repeats are repeated measurements of the same sample under the same conditions. Replicates are identical experimental units treated the same way. Both allow you to calculate a mean and assess the variability of your data.
重复是在相同条件下对同一样本进行多次测量。平行样是接受相同处理的相同实验单元。两者都使你能够计算平均值并评估数据的变异性。
Randomisation reduces bias. For example, randomly assign potato cylinders from a mixed batch to different sucrose concentrations rather than putting all large pieces in one beaker. This prevents an accidental relationship between specimen size and treatment.
随机化可减少偏差。例如，将混合批次中的土豆圆柱随机分配到不同蔗糖浓度中，而不是把所有大块放入一个烧杯。这可以防止样本大小与处理之间出现偶然关系。
Include enough repeats, usually at least three to five, so anomalous results can be identified and ignored. More repeats reduce the effect of random error but increase cost and time, so choose a sensible number for the investigation.
包含足够的重复次数，通常至少三到五次，以便识别并剔除异常结果。更多重复可减少随机误差的影响，但会增加成本和时间，因此要为实验选择合理的重复次数。
8. Risk Assessment and Ethical Considerations | 风险评估与伦理考量
Before starting, identify hazards such as hot water, sharp scalpels, corrosive chemicals or biological materials. State how to reduce each risk, for example by using low concentrations, wearing goggles or cutting away from the body.
开始前，要识别危险，如热水、锋利解剖刀、腐蚀性化学品或生物材料。说明如何降低每项风险，例如使用低浓度、佩戴护目镜或朝远离身体的方向切割。
If organisms are used, minimise harm and follow ethical guidelines. Replace animals with plant material where possible, reduce sample numbers and refine procedures to avoid distress. For microbial work, use aseptic technique and avoid culturing pathogens.
如果使用生物体，应尽量减少伤害并遵循伦理准则。尽可能用植物材料替代动物，减少样本数量并改进操作以避免痛苦。对于微生物工作，使用无菌技术并避免培养病原体。
Dispose of microbial cultures safely by autoclaving, and disinfect work surfaces before and after practical work. Ethical issues should be recorded in the plan because examiners look for evidence of responsible biology.
通过高压灭菌安全处理微生物培养物，并在实验前后对工作台进行消毒。伦理问题应记录在计划中，因为考官希望看到负责任的生物学实践证据。
9. Collecting and Recording Data | 数据收集与记录
Record data in a table with clear headings and units. The table should have space for repeats and means. Use the appropriate precision from the measuring instrument, such as 0.1 cm³ for a 10 cm³ measuring cylinder or 0.1 g for a simple balance.
在表中记录数据，表头清晰并注明单位。表格应为重复值和平均值留出空间。使用测量仪器相应的精度，例如 10 cm³ 量筒为 0.1 cm³，简易天平为 0.1 g。
Choose quantitative data where possible because it can be analysed statistically. If data are qualitative, define categories before starting so that different observers record results consistently.
尽可能选择定量数据，因为可以统计分析。如果数据是定性的，在开始前定义类别，使不同观察者能够一致地记录结果。
Time course measurements are useful for rates. For example, measure gas volume every 30 seconds to calculate the initial rate from the steepest part of the progress curve. This reduces the influence of product inhibition or substrate depletion later in the reaction.
时间进程测量对速率很有用。例如，每 30 秒测量一次气体体积，从进程曲线最陡部分计算初始速率。这可以减少反应后期产物抑制或底物耗尽的影响。
10. Descriptive Statistics and Uncertainty | 描述统计与不确定性
Calculate a mean to summarise repeated measurements. The mean gives a central value but does not show spread, so it should be reported together with a measure of variability.
计算平均值以汇总重复测量结果。平均值给出中心值，但不显示离散程度，因此应与变异性的度量一起报告。
mean = Σx / n
Calculate the sample standard deviation to show spread around the mean. A large standard deviation indicates that repeated values differ widely.
计算样本标准差以显示平均值周围的离散程度。标准差大表明重复值差异很大。
SD = √(Σ(x – mean)² / (n – 1))
Percentage change is useful when initial values differ, such as the change in mass of potato strips placed in different sucrose solutions.
当初始值不同（如土豆条在不同蔗糖溶液中的质量变化）时，百分比变化很有用。
percentage change = (final – initial) / initial ×
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