Those sorts of questions are asked in a field called Information Visualization, which is a part of Human Factors Engineering.

Don't have a formal source, but I can give you a quick rundown of the advice my group ends up giving to every student we work with: * Label the dang axes. * Make the axis labels bigger. * Make histogram lines thicker; make dots larger. * If the dots are very dense, don't use dots, use a color scale. * For the sake of the absent gods, don't make your colour scale brown-yellow-lightgray-black-darkbrown-darkgray-darkyellow, as one often-used plotting package did by default. (It was an inheritance from the early nineties, and honestly it was still weird.) Make it something that humans naturally read as a scale, eg blue to red by way of violet, dark green to light green, or blue to red by way of the rainbow. * On a white background, do not use yellow or bright green unless the individual dots or areas are large. Lines, generally speaking, are not large. * Put a legend in one corner, explaining what the line styles mean. * If you're using (eg) triangles for one data type and circles for another, make the points bigger. Yes, it likely looks perfectly clear on your screen, to your young eyes, at a distance of a foot. You will eventually present it on a crappy twenty-year-old projector to men of sixty and seventy sitting at the back of a large auditorium. EMBIGGEN THE DANG POINTS. Also, use colours to further clarify the difference, unless colour is indicating a different dimension of information. * Make bin sizes a round number - 1, 2, or 5 - in a unit of interest. * If plotting numbers of something, indicate the bin size by labeling the y axis (for example) "Events / 2 MeV". * As a general rule, make both a linear and a semilog plot. You can skip the linear if there are no features of interest at high densities, and the semilog if there are no features of interest at low densities.