📚 Two Methods of Error Detection in IGCSE Science | IGCSE科学中两种误差检测方法
In IGCSE Science experiments, obtaining accurate and reliable results is essential. However, errors can occur due to various factors, such as limitations of instruments, environmental conditions, or human mistakes. Detecting these errors is the first step towards minimising their impact. This article explores two fundamental methods of error detection: analysing repeated measurements to spot random errors, and comparing results with a known standard to identify systematic errors.
在IGCSE科学实验中，获得准确可靠的结果至关重要。然而，误差可能由于仪器的局限性、环境条件或人为错误等多种因素而产生。检测这些误差是减少其影响的第一步。本文探讨两种基本的误差检测方法：通过分析重复测量来发现随机误差，以及通过将结果与已知标准对照来识别系统误差。
1. Understanding Errors in Science | 理解科学中的误差
In any scientific investigation, experimental errors can be divided into two main categories: random errors and systematic errors. Random errors cause readings to be scattered around the true value and are often due to unpredictable fluctuations. Systematic errors, on the other hand, shift all measurements in the same direction (either consistently too high or too low) and are typically caused by faulty equipment or poor experimental design.
在任何科学探究中，实验误差可分为两大类：随机误差和系统误差。随机误差导致读数在真实值附近分散，通常是由于不可预测的波动造成的。而系统误差则会使所有测量值朝同一方向偏移（始终偏高或始终偏低），通常由故障设备或不良实验设计引起。
Detecting these errors requires different approaches. Random errors become visible when the same measurement is repeated multiple times and the results vary. Systematic errors can only be detected by comparing your results with a known true value or by using a different method.
检测这些误差需要不同的方法。当同一个测量重复多次且结果不一致时，随机误差就会显现出来。系统误差只能通过将你的结果与已知真值进行比较或使用不同方法才能发现。
The following table highlights the key contrasts between these two error types, aiding in their identification during experiments.
下表突出显示了这两种误差类型之间的主要差异，有助于在实验中识别它们。
| Feature (特征) | Random Error (随机误差) | Systematic Error (系统误差) |
|---|---|---|
| Effect on readings (对读数的影响) | Readings vary above and below the true value in an unpredictable way | All readings are shifted in one direction (all too high or all too low) |
| Detection method (检测方法) | Take repeat measurements and examine the spread (range) | Compare with a known standard, use a different instrument or calibrate |
| Example (示例) | Reaction time variations when using a stopwatch | A balance that has not been zeroed, giving a constant offset |
2. Method 1: Repeated Measurements to Detect Random Errors | 方法一：通过重复测量检测随机误差
Random errors cause individual readings to differ from one another even when the same quantity is measured under the same conditions. The simplest way to detect random error is to take several repeat readings and examine the spread of results. If the readings vary significantly, random errors are present.
随机误差会导致即使在相同条件下测量同一量时，各个读数也会彼此不同。检测随机误差的最简单方法是进行多次重复读数并检查结果的分散程度。如果读数差异显著，则存在随机误差。
For example, when measuring the time for a pendulum to complete ten swings, you might record 12.3 s, 12.5 s, 12.4 s, and 12.6 s. The slight variation indicates random errors, likely caused by reaction time when starting and stopping the stopwatch.
例如，在测量摆锤完成十次摆动的时间时，你可能记录到12.3秒、12.5秒、12.4秒和12.6秒。微小的差异表明存在随机误差，很可能是由启动和停止秒表时的反应时间引起的。
Repeated measurements not only reveal random errors but also allow you to calculate a mean value, which is a better estimate of the true value than any single reading.
重复测量不仅能揭示随机误差，还能让你计算平均值，这比任何单一读数都能更好地估计真实值。
3. Carrying Out Repeated Measurements Correctly | 正确进行重复测量
To detect random errors effectively, you should repeat the measurement at least three times. Make sure to reset and redo the procedure independently each time, not just re-read the same display. Record all readings to the same precision.
为有效检测随机误差，你应至少重复测量三次。确保每次独立重置并重新进行步骤，而不是仅仅重新读取同一显示。记录所有读数并保持相同的精确度。
After taking the readings, list them in a table. Then calculate the arithmetic mean: add all values together and divide by the number of readings. You can also find the range (maximum minus minimum) to assess the spread.
记取读数后，在表中列出它们。然后计算算术平均值：将所有值相加并除以读数次数。你还可以计算极差（最大值减最小值）来评估分散程度。
mean = (sum of all readings) / (number of readings)
平均值 = 所有读数之和 ÷ 读数次数
range = largest reading − smallest reading
极差 = 最大读数 − 最小读数
A small range suggests good precision, but it does not guarantee accuracy if a systematic error is present.
小的极差表明精密度好，但如果存在系统误差，这并不能保证准确度。
4. Identifying Anomalous Results | 识别异常值
When you plot the repeated readings or simply look at the list, one reading may be very different from the others. This is called an anomalous result. It can be caused by a momentary mistake, such as misreading the scale or recording the wrong number. Anomalous results indicate a possible error that should be investigated.
当你绘制重复读数图或只是观察列表时，某个读数可能与其他读数差异很大。这称为异常值。它可能是由瞬间错误引起的，例如读错刻度或记录错误数字。异常值表明可能存在需要调查的错误。
If you identify an anomalous result, you should check your recording, and if the reading is confirmed to be a mistake, exclude it from the calculation of the mean. However, you must never simply remove a result just because it does not fit your expectation; always try to repeat the measurement to see if the anomaly was a one-off error.
如果识别出异常值，你应检查记录，如果确认该读数是错误，则在计算平均值时将其排除。但是，绝不能仅仅因为某个结果不符合预期就将其删除；务必尝试重复测量，看该异常是否是一次性错误。
It is good practice to record all raw data and indicate clearly if any value is excluded, along with the reason.
良好的实践是记录所有原始数据，并清楚地指出是否排除了任何值以及排除的原因。
5. Method 2: Comparison with a Known Standard to Detect Systematic Errors | 方法二：与已知标准对比检测系统误差
Systematic errors are much harder to detect simply by repeating measurements, because all readings are shifted in the same direction
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