Reaching Conclusions

Purpose of Reaching Conclusions

Reaching conclusions is the final step in fieldwork where you summarise your findings clearly and directly. The aim is to answer the enquiry questions set at the start of your investigation by linking your collected data back to your initial hypotheses or predictions.

For example, if your hypothesis was that river velocity increases downstream, your conclusion should state whether your data supports this and explain why based on your results.

This step is essential because it shows how your fieldwork has helped you understand the geographical processes or patterns you were investigating.

Interpreting Data

Interpreting data means looking closely at your results to identify patterns, trends, and any unusual points (anomalies). This helps you understand what your data is showing about the geographical question.

• Patterns and trends: Look for consistent changes or relationships in your data. For example, does the amount of vegetation cover increase with distance from an urban area?
• Anomalies: Spot any data points that don’t fit the pattern. These might be due to errors or unique local factors and should be considered carefully.
• Statistical measures: Use averages like the mean, median, or mode to summarise data sets. These help describe typical values and reduce the effect of anomalies.

For instance, if you measured soil pH at different points along a river, calculating the mean pH at upstream and downstream sites can show if acidity changes consistently.

Example: Suppose you recorded river velocity at five points downstream: 0.5, 0.7, 0.9, 1.1, and 1.3 $\mathrm{m\,s^{-1}}$. The trend shows velocity increasing downstream, supporting the hypothesis that velocity rises as the river flows further.

Drawing Conclusions

When drawing conclusions, you must:

• Relate findings to geographical theory: Explain how your results fit with what you know about physical or human geography. For example, increasing river velocity downstream matches the theory that rivers gain volume and energy as tributaries join.
• Assess reliability and validity: Consider how trustworthy your data is. Reliable data is consistent and repeatable, while valid data accurately measures what you intended.
• Explain the significance: Discuss why your results matter. What do they tell you about the environment or human activity? How do they help answer your enquiry question?

For example, if your data shows more litter in urban parks than rural ones, you could conclude that human activity increases pollution, which is important for local council planning.

Example: In a study of coastal erosion, you found that erosion rates were highest on exposed headlands compared to sheltered bays. This supports coastal geography theory that wave energy is stronger on headlands, causing more erosion. The data is reliable because measurements were repeated over several days, and the conclusion is significant for coastal management decisions.

Limitations and Improvements

No fieldwork is perfect. Recognising the limitations of your data collection helps you understand how your conclusions might be affected and how future studies could improve.

• Data collection constraints: These include time limits, weather conditions, equipment accuracy, and sample size. For example, bad weather might have limited access to some sites or affected measurements.
• Method improvements: Suggest ways to reduce errors or collect more detailed data next time. This might mean using more precise instruments, increasing sample numbers, or choosing different locations.
• Environmental and human factors: Consider how natural changes (like tides or seasons) or human actions (construction, foot traffic) could have influenced your results.

Example: If you measured footpath erosion in a park but only sampled on one day, your data might not represent long-term trends. Next time, sampling over different seasons would improve reliability.