presenting data - graphs , tables , bar charts , histograms and scatter graphs

Purpose of Presenting Data

• To summarise, organise, and display data clearly so that patterns, trends, and relationships can be easily seen.

• Data presentation helps in the analysis and interpretation of results.

• Visual displays (graphs/charts/tables) make results easy to compare and understand.

Tables

• Used to organise raw data or summarise descriptive statistics (e.g., mean, median, mode, range).

• Often the first step before drawing graphs.

• Should include:

  • Clear headings (with units where appropriate).

  • Rows and columns neatly labelled.

  • Totals, averages or summary data if relevant.

• Avoid including unnecessary decimal places — keep data clear and consistent.

Graphs

• Used to display quantitative data visually.

• The type of graph chosen depends on the level of measurement and type of variable (discrete or continuous).

Key features (for all graphs):

• Title (clear and descriptive).

• Labelled axes (with units).

• Appropriate scale (equal intervals).

• Accurate plotting of data points.

• Neat, clear, and correctly labelled key/legend if needed.

Bar Charts

• Used for discrete (separate) data — where categories are distinct (not continuous).

• Typically used for nominal or categorical data.

• Each bar represents the frequency or value for each category.

Key features:

• Bars are separate — gap between bars.

• X-axis: Categories or conditions (independent variable).

• Y-axis: Frequency, percentage, or mean score (dependent variable).

• Bars must be the same width and equal spacing.

Histograms

• Used for continuous data — data that flows smoothly from one value to another (e.g., time, height, scores).

• Typically used for interval or ratio data.

• Shows the frequency distribution of a continuous variable.

Key features:

• No gaps between bars (continuous data).

• X-axis: Continuous variable (e.g., score intervals or ranges).

• Y-axis: Frequency (number of participants or occurrences).

• Each bar’s width represents an interval, and height shows frequency.

Scattergrams

• Used to display relationships (correlations) between two co-variables.

• Each point represents one participant’s scores on the two variables.

Key features:

• X-axis: One variable (e.g., hours of sleep).

• Y-axis: Second variable (e.g., concentration score).

• Each dot = one participant (plotted pair of scores).

• A line of best fit can be drawn to show trend.

Interpretation:

• Positive correlation: as one variable increases, the other also increases.

• Negative correlation: as one variable increases, the other decreases.

• No correlation: no pattern or trend.

choosing the correct data to display

Tables →

• Used to organise raw data or summarise descriptive statistics (e.g., mean, median, mode).

• Good for any data type — foundation for graphs.

Bar Chart →

• Used for discrete (separate) data.

• Shows differences between categories or conditions.

• Gaps between bars.

• Data usually nominal or ordinal.

• Example: comparing mean scores for Group A vs Group B.

Histogram →

• Used for continuous data.

• Shows frequency distribution of scores.

• No gaps between bars.

• Data must be interval or ratio.

• Example: distribution of reaction times or test scores.

Scattergram (Scatterplot) →

• Used to show relationships/correlations between two continuous variables.

• Each point = one participant’s two scores.

• Can show positive, negative, or zero correlation.

• Example: relationship between stress level and illness score.