Inquiry Skills: Investigations, Data Analysis, and Evaluating Conclusions
Designing and reporting investigations
A strong investigation states a testable hypothesis, identifies independent, dependent, and controlled variables, and describes a reproducible method with appropriate sample size and replication. Recognise random versus systematic error and the difference between accuracy (closeness to true value) and precision (closeness of repeated measurements). Use control groups where relevant and consider ethics and safety in field and laboratory work.
Analysing data
Select the right presentation: line graphs for continuous change, scatter plots for relationships, bar charts for categories. Calculate mean, median, and range, and use uncertainty and significant figures honestly. Look for trends, anomalies, and correlations, and remember that correlation does not prove causation. Apply statistical significance ideas: a difference is more convincing when sample sizes are large and variability is small.
Evaluating conclusions
Assess whether a conclusion is supported by the evidence, whether the sample is representative, and whether confounding variables could explain the result. Identify limitations and suggest improvements. Distinguish primary from secondary data and consider the reliability of sources.
Common traps
Students often confuse accuracy with precision, or claim causation from a correlation. Another trap is ignoring units or uncertainty when comparing values. Always check whether the conclusion overreaches the data.
Worked example
Three trials give 12, 14, and 16 units. Mean = 14, range = 4, so uncertainty is roughly ±2 units. A claim that the true value is 20 lies outside this range and is not well supported.