Evaluating data
The quality of any data should be evaluated before making any conclusions.
Precision, repeatability and reproducibility
| Term | Meaning |
| Precision | Measurements are in close agreement |
| Repeatable | Measurements are very similar when repeated by the same person or group, using the same equipment and method |
| Reproducible | Measurements are very similar when repeated by a different person or group, using different equipment and/or methods |
| Term | Precision |
|---|---|
| Meaning | Measurements are in close agreement |
| Term | Repeatable |
|---|---|
| Meaning | Measurements are very similar when repeated by the same person or group, using the same equipment and method |
| Term | Reproducible |
|---|---|
| Meaning | Measurements are very similar when repeated by a different person or group, using different equipment and/or methods |
Precision and repeatability can be seen easily from a table of results containing repeat measurements. If the repeat measurements are close together, the data is precise and repeatable.
Accuracy
Evaluation of the data should also consider How close a numerical result is to the true value.. A measurement is accurate if it is close to the The actual value that a measurement should be..
To ensure the data is as accurate as possible, work out the best estimate of the true value:
- Identify any A measurement that appears very different to other repeat measurements. It should be included in the data unless a reason is found to explain it. (anomalous results) in the data. These are results that are very different to the others. For example:
- Try to explain why the outlier is different. An outlier may be removed if there is a good reason to do so. For example, if there is a measurement or recording error.
- Find the The mean is calculated by adding all of the data and dividing by the number of items of data. of the remaining results. To find the mean add together the results and divide by the number of measurements.
Example
Using the example above:
Sum of values = 0.9 + 1.0 + 1.2 + 0.8 + 1.0 + 1.1 + 0.8 + 1.2 = 8.0
Number of measurements = 8
Mean = 8.0 梅 8 = 1.0
This mean is the best estimate of the true value.
The mean should be given to the same number of Giving a number to a specified number of significant figures is a method of rounding. For example, in the number 7483, the most significant, or important, figure is 7, as its value is 7000. To give 7483 correct to one significant figure (1 sf), would be 7000. To 2 sf, it would be 7500. as the measurements in the table.
Confidence in the accuracy of results
Data cannot always be relied upon. There can be errors, and all measurements have some level of uncertainty.
An error in measurement where results vary in an unpredictable way. This is sometimes due to human judgement. are unpredictable and can be due to human error, eg in judging when to stop a timer.
An error in measurement which differs from the true value by the same amount each time.This could be due to the equipment, how the experiment is 'carried out', or the environment. cause results to differ from the true value by the same amount each time. These could be due to:
- a fixed error in the measuring instrument, eg not being correctly zeroed
- influence of the environment, eg allowing a reaction to take place at a hotter temperature
- method of observation, eg not reading the volume of a liquid correctly using the bottom of the The curve in the upper surface of a liquid contained within a glass container. Where the container is narrow, like a burette, the meniscus is most noticeable.
To describe the accuracy of an experiment, discuss the level of confidence in the results. If only one reading was taken, then you can be less confident in the accuracy of the results.
The Range is the difference between the highest and lowest values in a set of data. describes the difference between the highest and lowest repeat results. The smaller the range, the more confident you can be in the accuracy of the result.
Evaluation of experimental strategy
Having evaluated the data, suggest improvements to the way in which the experiment was carried out that could improve the quality of the results.