4.4.2 (HL)—Graphs and results

A graph is a visual model of data, not decoration. The variable type and question determine the display: categories are separated, continuous intervals touch, time trends follow an ordered axis, and two measured variables can be compared as a scatterplot.

Bar graphs suit categorical or discrete conditions and leave gaps; histograms show continuous frequency distributions with touching bars; line graphs show change across ordered time points; scatterplots show the direction and strength of an association between two co-variables.

If three memory conditions produce mean scores, a bar graph compares the conditions. If temperature is recorded each day, a line graph shows the time trend. A scatterplot of stress and sleep can slope downward without proving that changing sleep would cause stress to fall.

A graph cannot repair poor sampling, misleading axes or confounded measures. Check units, scale, labels and spread before describing a pattern; correlation in a scatterplot is not causation.

Use an interpretation chain: identify variables, units, groups and sample sizes; read exact table values before summarising the pattern; inspect centre, spread, outliers and distribution; interpret an inferential result or effect size without treating significance as importance; then connect the result to the design, validity, bias and the conclusion. A result supports only the comparison or association actually measured.