Writing a Results and Discussion section

You will write a combined Results and Discussion section. However, you should know the different functions of each part. The two parts do not need to be separated by headings.

Keep you audience in mind when you write. Ostensibly, your audience is a general reader of Biochemistry, however you should also explain what happened with enough detail that your instructor can see that you understand the phenomena at play. 

Figures, tables, and schemes

In general chemistry (CHE 13X) labs and papers in my liberal studies courses, place figures, tables, and schemes after the paragraph in which they are first mentioned.

In my CHE 2XX and 3XX labs, you may simply present figures, tables, and schemes together at the start of the Results and Discussion section. When you submit a manuscript to a journal, you are responsible for the content only, professional layout designers will integrate your text and graphics appropriately to make sure that each graphic is placed where it fits on the page after its first mention in your text. Lab reports in these courses have so many figures and tables that they often break up the text too often. We do not want you spending any time on layout design to deal with this issue. If you prefer to incorporate your figures, tables, and schemes into the report text you will not be penalized.

  • Number figures, tables, and schemes consecutively in order of appearance in the text.
  • Make sure that each image and its associated text is visually separate. It is good practice to separate each item with horizontal lines.
  • See Appendix A: Data Presentation for details on the appearance of these.

Text

A. Always begin the text of this section by restating the objective of the experiment and whether or not you successfully achieved that objective.

B. The discussion of each experiment conducted should follow this formula...

  1. Briefly describe what was done
  1. Present the results
  • Describe what each of your figures and tables reveal (e.g. "Figure 1 reveals... "). Often the meaning of a piece of data is not obvious to someone who didn't perform the experiment. Guide your reader.
    • Example: "Figure 1 plots the relationship between polarity and protease activity for the substrates."
    • Do not list information from tables in sentence form. The whole point of a table is to visually organize information. Simply say describe the information in the table. Example: "Table 1 lists the enzyme inhibition activity and characterization data for the benzoxaxinones."
  • Refer to each and every figure or table in your text or expect to lose points.
  • See Appendix A: Data Presentation for details.
  1. Comment on the quality of your results and explain whether quality limits the ability to make a conclusions from it.
  • When commenting on quality of data, you should set some criteria for judgement and state whether or not your data meets that criteria.
  • Quality is function of precision, accuracy, and the extent to which samples were omitted. Clearly state which of these parameters you are evaluating and your quality criteria.
    • Precision. Always compare the relative standard deviation (RSD) rather than the absolute standard deviation. In biochemistry, <10% RSD is generally precise enough.
      • Example: The value 4 ± 2 U has 50% error and 4.0 ± 0.3 U has 8% error. Although both values are the same, the high relative error for the former value indicates a poorer quality result.
    • Accuracy. When available, compare the similarity of measured values to either known controls (e.g. positive and negtive controls). When these are not available, values still may be judged as potentially inaccurate if your trendline was imprecise.
      • Typical criteria: For a series of measurements based on the same standard curve in biochemistry, an R2 > 0.98 generally suggests that the derived values are accurate, R2 < 0.94 generally suggests low accuracy, and R2 values in between suggest moderate accuracy. Ask your instructor or TA if you are unsure how to evaluate accuracy in a particular experiment.
    • Omitted data. If you had to omit data because of significant outliers, this also suggests poor quality in the dataset.
      • When fitting any model to data, it is assumed that each data point provides equally precise information about the deterministic part of the process and thus has the same relative standard deviation.4
      • As a rule, only omit data points if you can justify that they are not equally precise as the primary data.
      • You should invalidate data if you know that you made a specific mistake with a sample, explain what occured.
      • You should invalidate data if they are measured values that fall outside your standard curve.
      • Otherwise, apply a statistical test. Deleting trendline data simply to improve R2 is not a valid justification.
      • You may omit data without a statistical test if it is data that you expect to deviate from a trend for technical reasons (e.g. the non-linear time points for initial rate kinetics).
      • If you rejected any outlier data, you must present your omitted data along with the data you used and justify your reasons for omission.
  • If you judge the results to be of poor quality,
    • you are ethically required to state this to reader and explain how the results limit confidence in your conclusions
    • and you must demonstrate your understanding of the concepts behind the lab by suggesting what went wrong.
    • If you are unsure what went wrong, propose a testable hypothesis that would potentially uncover the reasons for your poor quality data.
  • Direct the reader to important values or trends in the data.
  • Describe general trends and highlight interesting values.
    • Do not repeat a list of data from your tables, you created a table to avoid confusing lists of values in text.
    • Do not state the trendline equations from your captions in your text unless you have a compelling reason to do so.
  • If data are expected to follow a trend, be clear about this expectation and describe how closely the data follow it.
  • Never fit a straight line to non-linear data.
  • If you are not sure what to say, discuss highest and lowest values.
  • If you claim that two values are different, make sure that their uncertainty ranges do not overlap. Statistically, the two values may be the same.
    • Example: The values 55 ± 7 U and 65 ± 7 U are not statistically different.
  • Demonstrate that you understand what happened.
    • Example: Instead of saying "none of the lanes resembled one another", explain that "lane 1 showed X indicating Y happened when preparing the sample."
  • When possible, relate your results to existing knowledge in the field.
  • Compare your results to the most relevant values in the published literature.
  • State what is different and similar about the conditions between the experiments.
  • If the values differ significantly. provide a possible explanation.
  • Always cite the source of your literature value.
  • Interpret the data in terms of your objectives.

C. After discussing each experiment, summarize your discoveries.

D. Conclude by suggesting further work that should be performed.

  1. A great scientist is not someone who knows the right answers, but someone who asks the right questions. Every scientific experiment creates more new questions than answers. These last sentences of your paper set the stage for the next scientific explorations.
  2. If you did not completely accomplish your goals, identify what may have gone wrong, suggest a specific improvement, and explain why your improvement would reduce the effect of the problem you encountered.
  3. If you see no need for improvement, briefly suggest a follow-up experiment that builds on the current work.