I am supervising a project by a group of students who are quite new to academic/scientific writing. I have now read a draft of their report, and noticed the following errors in regards to how they use figures:

  1. The meaning of the y-axis is shown in the title, rather than as a y-axis label.
  2. The x-axis label is shown on the right of the x-axis tick values.
  3. The legend contains information that should be in the y-axis label, e.g., Policy 1 inventory level, Policy 2 inventory level.
  4. The figures are placed in the middle of the text without a figure number and a caption.

My question is: Is there is a good reference or set of guidelines which I could pass to the students to help them to learn how to properly include figures in their report?

  • 2
    Are they UG/PG/PhD students? Your institution will most likely have resources of this sort that you can refer them to. Especially if they are UG's they will benefit from adhering to the institutions/department's standards. But my most pressing question is- why can't you just give them that feedback yourself? Strikes me as a minor issue.
    – marts
    Jul 28, 2016 at 16:26
  • The APA is a good resource generally (specific to Psychology, yet widely used elsewhere): psych.utoronto.ca/users/reingold/courses/resources/handouts_apa/… makes some references, as does: coloradocollege.edu/dotAsset/… But a cite direct from APA/MLA guidance resources would be better, thus this is just a comment to help others find better resources.
    – BrianH
    Jul 29, 2016 at 2:27
  • Tufte has some very good commentaries on graphics. It won't explicitly cover the conventions, but if they learn the substance, they will come naturally.
    – Davidmh
    Jul 29, 2016 at 14:49
  • Better writing comes with time and with reading many papers in the field.
    – Nikey Mike
    Jul 29, 2016 at 16:17

1 Answer 1


Though the question refers to "including" graphs, the issues seem to be matters of labelling and presentation, which are part of graph design as a whole. The following are "the principles of graph construction" from William S. Cleveland's excellent if dated "The Elements of Graphing Data" (Wadsworth, 1985).


  • Make the data stand out. Avoid superfluity.
  • Use visually prominent graphical elements to show the data.
  • Use a pair of scale lines for each variable. Make the data region the interior of the rectangle formed by the scale lines. Put tick marks outside the data region.
  • Do not clutter the data region.
  • Do not overdo the number of tick marks.
  • Use a reference line when there is an important value that must be seen across the entire graph, but do not let the line interfere with the data.
  • Do not allow data labels in the data region to interfere with the quantitative data or to clutter the graph.
  • Avoid putting notes, keys, and markers in the data region. Put keys and markers just outside the data region and put notes in the legend or in the text.
  • Overlapping plotting symbols must be visually distinguishable.
  • Superposed data sets must be readily visually discriminated.
  • Visual clarity must be preserved under reduction and reproduction.


  • Put major conclusions into graphical form. Make legends comprehensive and informative.
  • Error bars should be clearly explained.
  • When logarithms of a variable are graphed, the scale label should correspond to the tick mark labels.
  • Proofread graphs.
  • Strive for clarity.


  • Choose the range of the tick marks to include or nearly include the range of the data.
  • Subject to the constraints that scales have, choose the scales so that the data fill up as much of the data region as possible.
  • It is sometimes helpful to use the pair of scale lines for a variable to show two different scales.
  • Choose appropriate scales when graphs are compared.
  • Do not insist that zero always be included on a scale showing magnitude.
  • Showing data on a logarithmic scale can improve resolution.
  • Use a scale break only when necessary. If a break cannot be avoided, use a full scale break. Do not connect numerical values on two sides of a break.


  • A large amount of quantitative information can be packed into a small region.
  • Graphing data should be an iterative, experimental process.
  • Graph data two or more times when it is needed.
  • Many useful graphs require careful, detailed study.

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