Complete the table to show whether each statement describes random errors or systematic errors.
| | Random error | Systematic error |
| :-------------------------------------- | :----------- | :--------------- |
| Cannot be predicted | | |
| Can be reduced by averaging results | | |
| Includes 'zero' errors | | |
Answer (1)
Random errors are unpredictable and can be reduced by averaging.
Systematic errors are consistent and include 'zero' errors.
'Cannot be predicted' and 'Can be reduced by averaging results' describe random errors.
'Includes 'zero' errors' describes systematic errors.
The completed table accurately classifies each statement. See table in step 3
Explanation
Analyze error types Let's analyze the characteristics of random and systematic errors to classify the given statements.
Random errors: These are unpredictable variations in measurements. They can be reduced by averaging multiple measurements.
Systematic errors: These are consistent and repeatable errors, often due to faulty equipment or experimental design. They cannot be reduced by averaging and include 'zero' errors (where the instrument doesn't read zero when it should).
Classify the statements
Cannot be predicted: This describes random errors because they fluctuate randomly around the true value.
Can be reduced by averaging results: This also describes random errors. Averaging helps to cancel out the random fluctuations.
Includes 'zero' errors: This describes systematic errors, as a 'zero' error is a consistent offset in the measurements.
Complete the table Based on the analysis, the completed table is:
Random error
Systematic error
Cannot be predicted
\checkmark
Can be reduced by averaging results
\checkmark
Includes 'zero' errors
\checkmark
Final Answer The completed table correctly identifies each statement as describing either random or systematic errors.
Examples
Understanding the difference between random and systematic errors is crucial in scientific experiments. For example, when measuring the length of an object multiple times, random errors might cause slight variations in each measurement. Averaging these measurements can reduce the impact of random errors, giving a more accurate result. On the other hand, if the measuring tape itself is slightly stretched (a systematic error), averaging won't correct the error; the tape needs to be calibrated or replaced. Recognizing and addressing these types of errors is essential for reliable and valid experimental results.
