Interpretation/Analysis of Data

Always begin with a clear overview of the data. This requires taking a step back and considering what the collective information tells you.
To fully understand the data, identify patterns or correlations. This involves comparing different variables to see if they seem to move or change together in a meaningful way.
Statistical analysis is an important tool for making sense of large datasets. Means, modes, medians, ranges, and standard deviations can provide crucial insights.
Never forget to critically evaluate the data. Look for any outliers or anomalies that might skew the interpretation.
Remember that correlation does not imply causation. Just because two variables move together doesn’t mean one causes the other to change.
Verify your interpretations by revisiting your original hypothesis. Ask whether the data supports the hypothesis, contradicts it, or neither.

Presentation and Conclusion of Findings

Graphs, tables and diagrams serve as effective ways to present the data clearly. This helps to visually demonstrate your findings.
A conclusion should summarise the main findings from your data. It should be directly related to the original hypothesis.
Remember to discuss the potential limitations of your data and methods, and offer recommendations for further research. This may include errors, uncertainty, or any factors that may have influenced the results.
Causation should be addressed in your conclusion. Describe how the evidence supports (or doesn’t support) the notion that changes in your dependent variable were caused by the independent variable.
Always refer back to the original purpose or question of the investigation. This gives the conclusion context and keeps it grounded in the original aims of the study.

Note the effectiveness of the investigation’s procedure. Did it work in the way it was supposed to?
Identify any sources of error and discuss how they may have impacted the results. These might have arisen from equipment, the environment or human error.
Debate the reliability and validity of your method. Could the investigation be repeated with the same results (reliable)? Does the method measure what it’s supposed to measure (valid)?
Suggest ways to improve the investigation and reduce potential errors. For instance, using more precise instruments or running more trials.
Consider the ethical implications of the method used, if any. Always think about the wider implications of your scientific investigations.