Types of Data

• Line Graphs


  • Most appropriate for continuous, quantitative data.
• Column Graphs


  • Most appropriate for discrete and/or qualitative data. Columns should be equal width and with a space between each bar.
• Gerda wanted to see how the length of a holly leaf affected the number of prickles that it had? What type of graph should be drawn?

  Quantitive, continous data- draw a line graph
• How do different indigestion powders neutralise acid in the stomach, What type of graph should be drawn?

  Qualitative- draw a column graph
• General Rules for Drawing a Graph

  • Independent variable is drawn on the horizontal x-axis. Dependent variable is drawn on the vertical y-axis. label each axis with a title of which variable it represents and the unit in which it is measured. Include a descriptive graph title that includes both the IV and DV. Include a key to show what the colours and symbols on the graph represent.
• Graph of Multiple Groups over Time


  • If graphing results from different groups over time (e.g. control group and experimental group), then draw a separate line for each group.
• Replication


  The process of running your test/experiment multiple times. Each repeat is called a trial. It involves having multiple samples/individuals for the experimental and control groups. Replication improves the reliability of the results. Allows the average to be calculated to reduce the impact of an outlier (reading that is drastically different from other trials) and random error.
• Calculating Averages


  Add the values of all of the trials to find the total and then divide the total by the number of trials.
• Precision


  How close two or more values from the repeat trials agree are with each other. The closer the values are, the more precise the data is.
• Standard Deviation


  A way of expressing how different the values are from the average of all the trials. The lower the standard deviation, the closer the values are to the average and therefore the more precise the data is.
• Error Bars on Graphs

  • Error bars are graphical representation of the standard deviation and therefore show how precise the data is. The smaller the error bars, the lower the standard deviation, the closer the values are to the average and therefore the more precise the data is.
• Constructing a graph- Summary

  Average: Calculate the average in Excel. Standard Deviation: Calculate the standard deviation in Excel. Error bars: Add error bars to the graph in Excel. Axis labels: Add axis labels to the graph in Excel. Chart title: Add a chart title to the graph in Excel.
• Experimental group- heart rate increases as the duration of exercise increases.
• Control group- heart rate remains relatively constant as the duration of exercise increases.
• Error bars- there are two longer error bars, here the data is less precise.
• Discussion- Data analysis and interpretation

  Remind reader about aim of investigation. Describe and interpret pattern in results (including some values). Compare results from experimental group to control group. Discuss what the results suggest or indicate. What are all possible conclusions that can be made from these results? Discuss whether the results support the hypothesis or not. Cannot say hypothesis was correct or incorrect.
• Discussion of Biological concepts

  Discuss the biological concepts that may explain the results. Cite secondary sources using APA in-text citations.
• Discussion- Limitations and Improvements

  Discuss limitations of the experimental design and methods and suggest improvements to the experimental design. Do not discuss human errors made in the experiment!
• Limitations- Accuracy


  How close is the measurement to the true value? Examples: Using a heart rate monitor instead of counting the pulse rate. Using more accurate scales. How could the accuracy of your experiment be improved?
• Limitations- Precision


  How closely do two or more values agree with each other when the experiment is repeated? Eg. Use same equipment for each measurement. How can precision be improved?
• Limitations- Repeatability


  Can similar results be achieved when replicating the method under the same conditions? Depends on how good experimental design and methods are and how accurate measurement is. How can repeatability be improved?
• Limitations- Validity


  Does the experiment measure what it is supposed to be measuring? Eg. The results can only be caused by a single independent variable. Results are not valid if experiment is not designed or controlled properly. How can validity be improved?
• Example discussion stems


  Restate aim and hypothesis. State main investigation result / findings using some numerical values. Discuss whether hypothesis was supported or not. This should be justified using specific details selected from the investigation findings. Summarise limitations of the investigation design and suggested improvements or future experiments.
• Checkpoint


  Discussion: Aim, Pattern/trend, Compare control and experimental group, What do the results suggest?, Do the findings support the hypothesis?, Biological concepts, Limitations- at least two, Conclusion: Restate aim & hypothesis, Main findings, Is the hypothesis supported or not?, Summarise limitations