fieldwork: analysis, conclusions and evaluation

Answer: The mean is the sum of all values divided by the number of values; the median is the middle value when data is arranged in order. The mean is more appropriate when data has no extreme outliers and you want to use all data values (e.g. average river velocity at a site). The median is more appropriate when there are outliers that would distort the mean (e.g. environmental quality scores where one extremely high or low score would pull the mean but not affect the median). In the example with scores including a 45 outlier, the median (20) better represents the typical score than the mean (22).

Answer: The mean is the sum of all values divided by the number of values; the median is the middle value when data is arranged in order. The mean is more appropriate when data has no extreme outliers and you want to use all data values (e.g. average river velocity at a site). The median is more appropriate when there are outliers that would distort the mean (e.g. environmental quality scores where one extremely high or low score would pull the mean but not affect the median). In the example with scores including a 45 outlier, the median (20) better represents the typical score than the mean (22).

Answer: The result of +0.82 indicates a strong positive correlation between the two variables. Since 0.82 exceeds the critical value of 0.65 at the 95% confidence level (p=0.05), the result is statistically significant. This means there is less than a 5% probability that the correlation occurred by chance. Therefore, you can reject the null hypothesis and conclude that there is a genuine positive relationship between the two variables.

Answer: Factor 1: Sample size - too few sites makes results unrepresentative and more affected by anomalies. Collecting data at only 3 river sites, for example, would not reliably show downstream trends. Factor 2: Measurement error - human errors in reading instruments (e.g. misreading a flow meter) or recording data (e.g. writing the wrong number) reduce reliability. This can be improved by taking repeat readings. Factor 3: Timing - results vary with conditions; river discharge after heavy rain would be much higher than during a dry period, making comparisons unreliable.

Answer: A conclusion should: (1) State whether the hypothesis is supported or rejected; (2) Summarise the key evidence using specific data values; (3) Link findings to geographical theory (e.g. the Bradshaw Model); (4) Explain any anomalies that didn't fit the pattern; (5) Acknowledge limitations that reduce certainty. The conclusion must directly answer the original question and be fully supported by evidence from the data and analysis.

Answer: Reliability means the results can be replicated - if you repeated the investigation under the same conditions, you would get similar results. It depends on consistent methods, adequate sample size, and accurate measurements. Validity means the results actually measure what was intended and genuinely answer the research question. Both are important because: unreliable results cannot be trusted (they may be random); invalid results may be reliable but measure the wrong thing (e.g. a well-conducted study that measures footfall instead of environmental quality). An investigation must be both reliable AND valid to produce meaningful conclusions.