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

  • Place figures, tables, and schemes after the paragraph in which they are first mentioned.
  • 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 Data Presentation (Appendix A) for details on the rules for presentation of these.

If your writing has so many figures and tables that they break-up the text too often or you are encountering layout difficulties, ask your instructor if they will accept presenting figures, tables, and schemes together at the end of the document without penalties. When you submit a manuscript to a professional journal, you are responsible for the content only and layout designers will integrate your text and graphics appropriately to make sure that each graphic, table, and scheme is placed where it fits on the page after its first mention in your text.

Equations

Equations are considered part of a sentence and should incorporated into your text following the formatting standards described in Data Presentation (Appendix A).

Text

A. Start this section by reminding the reader of your objectives by affirmatively stating what specific work was accomplished.

  • If your results are highly uncertain or you did not successfully accomplish any of your stated objectives, then you should open by stating your aspirational objectives and be clear about what you were and were not able to accomplish. As a matter of scientific ethics, your reader should learn upfront that you were unsuccessful or have low confidence in your results, rather than discover this in the concluding paragraphs.

  •  If multiple experiments were carried-out, you should write a separate Results and Discussion subsection for each experiment following the template of Part B below.

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

  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 Data Presentation (Appendix A) for details about presentation of uncertainty, units, and significant digits, as well as expectations for Table/Figure formatting.

  1. Comment on the quality of your results and explain whether quality limits the ability to make a conclusions from it. Quality is function of precision, accuracy, and the extent to which samples were omitted. When judging quality, you should set some criteria for judgement and state whether or not your data meets that criteria.
  • Omitting data.

    • As a rule, avoid omitting data unless you have a valid reason to do so.
    • 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 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 the measured values fall outside your standard curve.
    • Omit points that you expect to deviate from a trend for technical reasons (e.g. the non-linear time points for initial rate kinetics). 
    • It is not valid to delete trendline data simply to improve R2. You can often exclude outliers that lie far outside the expected trend by visual inspection. For data that are close to the trendline, you should apply a statistical test. This spreasheet demonstrates the standard deviation test for outliers.
    • If you rejected any outlier data, you must present your omitted data along with the data you used and justify your reasons for omission.
    • Additionally, if you omitted data because of significant outliers, you must comment on the poor quality of the dataset following the guidance below.
  • Precision.

    • Criteria for trendlines: For a series of measurements based on the same standard curve, an R2 > 0.98 generally suggests that the derived values are accurate in the sense that uncertatinty in the trendline itself is not contributing to the uncertainty of the derived value. An R2 < 0.94 generally suggests low accuracy in the sense that the most significant uncertainty may be the uncertainty from the trendline; an uncertainty derived sorely from the staandard deviation of replicate measurements probably doesn't reflect the true uncertainty. Finally, R2 values in between suggest moderate accuracy. Ask your instructor or TA if you are unsure how to evaluate accuracy in a particular experiment.

    • Criteria for measured values.  When comparing uncertainty values, always compare the relative standard deviation (RSD) rather than the absolute standard deviation.

      RSD=standarddeviationvalue×100%

      Measurements in the biological sciences tend have higher uncertainty than measurements in the physical sciences due to the inherent complexity and variability of biological systems. In the biological sciences (including biochemistry), <10% RSD is generally precise enough and <5% RSD is excellent. In the physical sciences, <5% RSD is expected for most measurements and <1% RSD is excellent.

      • Example 1: 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.

      • Example 2: The value 4 ± 1 U has 25% error (RSD) and 40 ± 1 U has 2.5% error. Although the SD is the same for both measurements, the precision differs significantly because the values differ. This fact is clearly communicated by the RSD values.

  • Accuracy.

    • To evaluate accuracy, you should compare your measured values to either published reference values or expected controls (e.g. positive and negtive controls). When these are not available, values still may be judged as potentially inaccurate due to an identifiable problem with either the experiment or the results. But without an expectation for comparison, you do not know if the results are actually inaccurate.

    • When making a comparison, two values are statistically different if their uncertainty ranges do not overlap. Otherwise, the two values may be statistically identical.

      • Example: The values 55 ± 7 U and 65 ± 7 U are not statistically different.

    • When you have no information about the uncertainty of a reported value, the convention is to assume that the uncertainty is ±1 in the position of the last digit

      • Example: The reported value 2.50 g is understood to mean 2.50 ± 0.01 g.

    • If two values are statistically different, you should use the relative percent error to compare your measured value to the expected value. To do this, calculate the difference between your value and the expected value, divide by the expected value, and multiply by 100%.

      PercentError=|Experimental-Theoretical|Theoretical×100%
      • Example: a measurement of 62 U is 28% smaller than an expected value of 77 U.

  1. State whether the quality meets expectations of precision and accuracy for the type of experiment conducted. Your objective is to orient the reader as to whether or not you have confidence in your results.
  • For good or fair data, you can simply use an adjective like "expected" or phrase like "adequate enough precision to conclude that there was a change between x and y" when you describe the quality.

  • But, 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;
    • you must demonstrate your understanding of the theoretical concepts of the experiment by suggesting an explanation for what went wrong;
    • if you are unsure of what went wrong, propose a testable hypothesis that would potentially uncover the reasons for your poor quality data.
  1. In relavent, 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 the data were expected to follow a trend, be clear about your expectations and describe how closely the data follow it.

  • Never attempt to fit a straight line to non-linear data.

  • If you are not sure what to say, discuss highest and lowest meaured values.

  • If the data do not make sense, demonstrate that you have a theoretical understanding of might have gone wrong.

    • Example: Instead of saying "none of the lanes resembled one another", explain that "lane 1 showed X indicating Y happened when preparing the sample."

  1. When possible, relate your results to existing knowledge in the field.

  • Compare your results to the most relevant values/results 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.
  • If you made a comparison to a literature value in your discussion of accuracy (see Step 3 above), you should incorporate this information in that discussion. 
  1. Interpret your results in terms of your stated objectives.

  • Make sure that is it clear what you have discovered or measured and why you did it.
  • Be open-minded to the fact that sometimes data sometimes defy your expectations and they are telling you something unexpected about the phenomenon under study. Consider, proposing a testable hypothesis to explain an unexpected result rather than assume you made a mistake.

C. Conclude by briefly summarizing your discoveries and 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.