Bar Charts and Line Graphs — When, How, and Common Errors

Test yourself on Fieldwork Presentation and Evaluation Skills with exam style questions, marked instantly.

Flashcards that come back just before you would forget them.

A revision plan that remembers what you know and shows what to do next.

See which topics you have nailed and which need another look.

Quiz yourself on Fieldwork Presentation and Evaluation SkillsGet your flashcardsBuild my revision planTrack my progress

📈 Bar Charts and Line Graphs — When, How, and Common Errors

Bar Charts

Bar charts are for discrete, categorical data — data where each category is separate and distinct (land use types, days of the week, named fieldwork sites). The height of each bar represents the value for that category. Because the categories are discrete, the bars should not touch.

Drawing a bar chart correctly:

x-axis — categories (land use type, site name, day of week). Label each bar clearly. The axis does not have a numerical scale.
y-axis — frequency, count, or value. MUST have a label AND units (e.g. "Number of pedestrians per hour"). Scale must start at zero.
Title — must refer to the enquiry question: "Pedestrian counts at five sites in Guildford town centre, 2pm, Tuesday" — NOT just "Pedestrian counts".
Bars do NOT touch — discrete categories are separate. Touching bars imply continuity between categories, which is false.

Variations on the bar chart:

  • Compound (stacked) bar chart — each bar is divided into stacked segments showing sub-categories. Useful when you want to show both total and the breakdown of that total (e.g. total traffic broken down by vehicle type at each site).
  • Deviation bar chart — bars extend both above and below a zero line. Used for bi-polar data (e.g. environmental quality scores that can be positive or negative).
  • Comparative bar chart — two or more bars side by side for each category. Allows direct comparison between groups (e.g. comparing pedestrian counts in summer vs winter at the same sites).

Line Graphs

Line graphs are for continuous data over time or distance — data where values are measured at regular intervals along a continuous scale (river depth at different distances from source, temperature across months, traffic counts across hours of the day). The line connecting points implies that the values vary continuously between your measurement points — which is why you should never use a line graph for discrete categorical data (e.g. do not connect "crime in London" to "crime in Birmingham" with a line — they are separate places, not points on a continuum).

Drawing a line graph correctly:

x-axis — the continuous variable (time, distance along transect). Must have a numerical scale with equal intervals.
y-axis — the measured value with units. Scale chosen to show variation clearly — avoid scales so compressed that all points appear flat.
Points and lines — mark each data point clearly (cross or dot), then connect them with a ruled line. The line shows trend, not exact values between measurements.
Multiple lines — if comparing two variables or two time periods, use different line styles (solid/dashed) or colours. ALWAYS include a clear legend.

Critical Error: Wrong Technique for the Data Type

The most common error is using a line graph for discrete categories, or using a bar chart for continuous time-series data. Ask: "Are my x-axis values genuinely continuous, or are they separate named categories?" If they are named categories (sites, land uses, areas), use a bar chart. If they are points on a continuous scale (time, distance), use a line graph.

Make this stick

Signing in gives access to all PrepWise features.

PrepWise is a trading name of A3M Quality Solutions Limited, registered in England and Wales, company number 14959172. Registered office: 202 Stroud Road, Gloucester, GL1 5LA. ICO registration ZC267069.