Describing your results
Describing bar charts
For each bar chart write a few sentences about which are the highest and the lowest bars. Include numbers from the bar chart to make the comparison clearer.
The bar chart below shows mean cross-sectional area at five sampling sites along a stream.
Site 1 has the lowest cross-sectional area (0.5 m2). Site 5 has the highest cross-sectional area (2.3m2). The cross-sectional area at Site 5 is approximately five times larger than the cross-sectional area at Site 1.
Describing line graphs
For each line graph write a few sentences about the trend that you can see. Is it a smooth line or a zig-zag?
The line graph below shows mean wetted perimeter with distance downstream from the source.
Wetted perimeter increases with distance from the source. It is 0.8 m at 0.5 km from the source and 2.4 m at 3.0 km from the source. However wetted perimeter does not rise steadily. Wetted perimeter does not increase much between 1.3 km and 1.8 km of the source, but there is a rapid increase in wetted perimeter between 1.8 km and 2.3 km of the source.
For each scattergraph write a few sentences about:
- the direction of the relationship (is it positive, is it negative, or is there no relationship?)
- the strength of the relationship (is is strong, or is it weak?)
- if there any anomalous results (points that lie a long way from the best-fit line)?
Explaining your results
Go through each table, chart and graph in turn. Write a few sentences to explain what you have just described. Try to use geographical words as much as you can.
The best explanations will give reasons for the strength of relationships and for any anomalous results.
Example: The scattergraph is a plot of mean cross-sectional area against mean velocity. There is a positive relationship between the two variables. As cross-sectional area increases, velocity also increases. The best-fit line shows that it is a fairly strong relationship. The points are close to best-fit line.
Why? An increase in cross-sectional area means that the river is becoming wider and deeper. A smaller proportion of the water in the river is in contact with the bed and banks. This means that less energy is lost to friction, so the water has more kinetic energy and can move faster.
It is not a perfect relationship because different shaped channels have different wetted perimeters. A square-shaped channel loses less energy to friction than a wide and shallow channel.
Go back to your aims, key questions or hypotheses
In this section you bring the threads together and answer your hypotheses.
Link your results together. For example, try to link results for width, depth and wetted perimeter with results for gradient.
If you have used secondary data, try to link this to the primary data that you collected.
Make sure you mention the evidence from your results that backs up each conclusion.
Finish off with something along the lines of...
"These results help to prove / disprove my initial hypothesis, which stated that... This is because..."
Go back through each one of your methods. For each, consider
- How accurate were your results?
- What were the sources of error?
- How has each source of error affected your results?
True value: the value that would be obtained in an ideal measurement
Accuracy: how close a measurement is to the true value
Error: the difference between the result that you found and the true value
What are the sources of error?
- Measurement error: mistakes made when collecting the data (such as someone misreading a clinometer)
- Operator error: differences in the results collected by different people (such as different people giving different environmental quality scores)
- Sampling error: where a sample is biased. Some elements of the population are less likely to be included than others.
Validity: the suitability of the method to answer the question that it was intended to answer
How do sources of error affect results?
Suggest how errors could have changed the results. Here is an example.
|Fieldwork technique||Questionnaires were given out to every 5th person seen in the High Street on Sunday morning|
|Bad evaluation||It was raining heavily when I gave out questionnaires|
|Good evaluation||It was raining heavily when I gave out questionnaires, so there were few people in the High Street except for rough sleepers. My sample was biased because shoppers were under-represented. Therefore the results of the questionnaire survey may not be accurate.|
Random error: these cause results to be spread about the true value. For example, imagine a student takes 20 temperature readings and mis-reads the thermometer for 2 of the readings. The effect of random errors can be reduced by taking more measurements.
Systematic error: these cause results to differ from the true value by a consistent amount each time the measurement is made. For example, imagine a student uses weighing scales which have not been zeroed, so all the results are 10g too high. The effect of systematic errors cannot be reduced by taking more measurements.
These are values in a set of results which are judged not to be part of the variation caused by random uncertainty.