Collecting Data

Planning Investigations

Investigations begin with clearly defined aims or questions. An understanding of what you want to find out helps to decide what data to collect.
The concept of independent, dependent, and control variables is crucial in the planning stage. The independent variable is what you purposely change, the dependent variable is what you measure as a result, and control variables are kept constant to ensure a fair test.

Measurement Techniques

Quantitative data are measurements that are taken as numerics and can be used for statistical analysis (e.g., height, mass, temperature).
In contrast, qualitative data involve observations that are descriptive and not easily measurable (e.g., colours, textures, presence or absence of features).
Some investigations may involve semi-quantitative data – these provide a rough measure of something which can then be ranked (e.g., categorising levels of pollution as low, medium, or high).

Accuracy and Precision

Accurate measurements are close to the true or accepted value. Accuracy is affected by systematic errors, which consistently skew results in a certain direction.
Precision refers to how closely repeated measurements agree with each other - this is affected by random errors which causes variability in results. The smaller the random errors, the greater the precision.
Using appropriate, well-maintained equipment and standardised techniques helps to improve both accuracy and precision.

Handling Data

Collected data needs to be carefully processed and organised for ease of analysis. This may involve calculation of mean values, ranges, and other statistical measures.
Make sure to handle data ethically, especially if the data is sensitive or personally identifiable. In these cases, respect privacy and gain informed consent if needed.

Dealing with Uncertainty and Errors

Every measurement has some degree of uncertainty associated with it, usually expressed as an error margin or confidence interval.
Try to identify potential sources of error and consider how they could affect the results - this is part of error analysis.
Having a sizable sample can help to minimise the effects of random errors and improve the reliability of results.

Replication and Verification

Repeating experiments and obtaining consistent results can increase confidence in the findings, this is known as replication.
Verification involves other scientists independently carrying out the same investigation to check whether they get the same results, enhancing the reliability and credibility of findings.

Collecting data is a foundational aspect of scientific enquiry. Whether planning an experiment, taking measurements, or interpreting results, researchers must meticulously collect and handle data to ensure their scientific investigations are meaningful and reliable.