Evaluating Data Quality
When analyzing experimental results, you need to determine if your data is accurate, precise, both, or neither. Let's practice with an example!
Michel's experimental values (2.59, 3.03, 3.21, 3.12, 2.85) are relatively close to each other, showing good precision. However, these values are far from the theoretical value of 6.85, indicating low accuracy.
This is a classic case of high precision, low accuracy - Michel's technique was consistent, but something in her experimental setup or calculations caused a systematic error.
Critical Thinking: When your results show high precision but low accuracy, look for systematic errors in your experiment setup or calculations!