Statistical Plots and Charts — Visualising Data Effectively

Introduction

In statistics, data are often easier to understand when shown in a visual form. Charts and plots allow us to see patterns, comparisons, and relationships that are less obvious in a list of numbers. At GCSE level, you are expected to read, interpret, and sometimes construct a wide range of different plots.

Each type of plot has its own purpose:

• Bar charts show comparisons between categories.
• Pictograms use repeated symbols or icons to represent frequency.
• Pie charts display proportions of a whole.
• Line graphs show trends over time or continuous change.
• Stem-and-leaf diagrams keep the original data visible while showing distribution.
• Histograms represent grouped continuous data using frequency density.
• Box plots (with cumulative frequency graphs) summarise spread and medians.
• Scatter graphs reveal correlations between two variables.
• Frequency polygons connect class midpoints to compare distributions.

In exams, you will be asked to:

• Draw a suitable plot from raw data or a frequency table.
• Interpret information shown on a plot.
• Compare two plots to comment on similarities and differences.
• Choose the most appropriate representation for a given data set.

In this tutorial we will go through each major GCSE plot type in turn. For each one we will define it, explain when to use it, give a step-by-step method, show worked examples (with diagrams), and highlight common exam mistakes. By the end, you will know exactly which plot to use and how to interpret them effectively.

Key Vocabulary

Before exploring each type of plot, let’s review the essential vocabulary that applies across different statistical diagrams. These terms appear frequently in GCSE exam questions.

• Axis (plural: axes) — The horizontal (x-axis) and vertical (y-axis) lines that frame a graph. Usually show categories, time, or measurement values.
• Frequency — The number of times a data value or category occurs. This is usually shown by bar height, line position, or symbol count.
• Scale — The numbering on an axis. A good scale is evenly spaced and chosen so all data fit neatly on the graph.
• Category — A label for a group (e.g. “Apples”, “Bananas” in a bar chart).
• Interval / Class — A group of values within a range (e.g. “10–19 minutes”). Used in grouped frequency tables, histograms, and frequency polygons.
• Midpoint — The middle of a class interval (e.g. midpoint of 10–19 = 14.5). Often used for plotting frequency polygons.
• Sector — A slice of a pie chart, representing a proportion of the whole.
• Correlation — The relationship between two variables in a scatter graph (positive, negative, or none).
• Outlier — A data point that lies far outside the rest of the data. Outliers can distort graphs (especially scales and ranges).
• Quartiles — Values that divide ordered data into four equal parts. Used in box plots (Q1 = lower quartile, Q3 = upper quartile).
• Median — The middle value of ordered data. Shown as a vertical line in a box plot.
• Frequency density — In histograms, bar height is calculated as \[ \text{Frequency density} = \frac{\text{Frequency}}{\text{Class width}} \] so areas represent frequency.
• Distribution — The overall shape of data (e.g. symmetric, skewed, uniform). Important when comparing plots.

Core Ideas

All statistical plots aim to represent data visually, but each type is suited to different kinds of data and questions. Understanding when and why to use each plot is just as important as knowing how to draw it. Here are the key concepts:

• Bar charts — Used for categorical or discrete data. Each bar represents a category; bar heights show frequency. Bars must be the same width and evenly spaced.
• Pictograms — Use pictures or icons to represent frequency. Each picture stands for a certain number of items (e.g. 1 picture = 5 people). Scale must be clear.
• Pie charts — Show proportions of a whole (out of 360°). Useful for percentages or fractions. Every sector angle = \(\frac{\text{Category frequency}}{\text{Total}} \times 360°\).
• Line graphs — Show trends in continuous data, often over time. Points are plotted and then joined by straight lines.
• Stem-and-leaf diagrams — Keep individual data values visible while showing distribution. Good for spotting medians, modes, and ranges quickly.
• Histograms — Represent grouped continuous data. Unlike bar charts, there are no gaps between bars. Heights show frequency density, not just frequency.
• Box plots — Summarise data using 5 values: minimum, Q1, median, Q3, maximum. Useful for comparing two distributions side by side.
• Scatter graphs — Plot pairs of values (x, y) to look for relationships. Used to describe correlation (positive, negative, none).
• Frequency polygons — Connect the midpoints of histogram classes with straight lines. Often used to compare two sets of data.

Each of these plots has strengths and weaknesses. For example, bar charts are simple but can’t show continuous data; histograms work well for large data sets but require careful use of frequency density. In exams, you’ll often be asked to choose the correct plot for a given context — so knowing the differences is vital.

Step-by-Step Method

Here we outline the main steps for constructing each of the major GCSE statistical plots. Follow these carefully in exams to maximise accuracy and method marks.

1. Bar Charts

1. Draw two perpendicular axes (x = categories, y = frequency).
2. Choose a clear scale for the y-axis that fits all frequencies.
3. Draw equal-width bars with gaps between them.
4. Label each bar with its category and the axes with titles/units.

2. Pictograms

1. Decide on a symbol (e.g. circle, stick figure, book).
2. Choose a key (e.g. 1 symbol = 2 people).
3. Draw the correct number of whole or partial symbols for each category.
4. Label clearly and always include the key.

3. Pie Charts

1. Find the total frequency.
2. Work out each sector angle: \(\text{Angle} = \frac{\text{Category}}{\text{Total}} \times 360°\).
3. Draw a circle and use a protractor to measure each angle.
4. Shade and label each sector; add a key if necessary.

4. Line Graphs

1. Draw axes (x = time or independent variable, y = measurement).
2. Choose an even scale for both axes.
3. Plot the data points accurately.
4. Join with straight lines (not curves unless specified).

5. Stem-and-Leaf Diagrams

1. Split each data value into “stem” (leading digits) and “leaf” (last digit).
2. Write stems in a vertical column.
3. Write leaves in rows next to the correct stem, in ascending order.
4. Add a key (e.g. 4 | 7 = 47).

6. Histograms

1. Work out class widths and frequencies.
2. Calculate frequency density for each class: \(\text{FD} = \frac{\text{Frequency}}{\text{Class width}}\).
3. Draw bars with no gaps; bar height = frequency density.
4. Label axes: x = class intervals, y = frequency density.

7. Box Plots

1. Find the 5 key values: minimum, Q1, median, Q3, maximum.
2. Draw a scale covering the full range.
3. Plot the 5 values above the scale.
4. Draw a rectangular “box” from Q1 to Q3 with a line at the median.
5. Extend “whiskers” from the box to min and max.

8. Scatter Graphs

1. Draw axes (x = independent variable, y = dependent variable).
2. Plot each data pair as a cross.
3. Look for patterns: upward = positive correlation, downward = negative correlation, random = no correlation.
4. Draw a line of best fit if asked.

9. Frequency Polygons

1. Find the midpoint of each class interval.
2. Plot a point at (midpoint, frequency).
3. Join points with straight lines.
4. Optionally connect to the x-axis at the ends to “close” the polygon.

Worked Examples — Foundation

Here we’ll go through examples of the most common statistical plots. Each example shows how the method is applied, with a simple GCSE-style data set. Static diagrams can be added where appropriate.

Example 1 — Bar Chart

The table shows favourite fruits of 20 students:

Draw a bar chart with fruits on the x-axis and frequency on the y-axis. Each bar should be the same width and evenly spaced.

Example 2 — Pictogram

Using the same data, draw a pictogram where 1 🍎 = 2 students. - Apple: 3 apples - Banana: 2 bananas - Orange: 3.5 oranges - Grapes: 1.5 grapes Include a key showing “1 symbol = 2 students”.

Example 3 — Pie Chart

For the fruit data, total = 20 students. Calculate sector angles: - Apple: \( \frac{6}{20} \times 360° = 108° \) - Banana: \( \frac{4}{20} \times 360° = 72° \) - Orange: \( \frac{7}{20} \times 360° = 126° \) - Grapes: \( \frac{3}{20} \times 360° = 54° \) Draw a circle with these sectors.

Example 4 — Line Graph

A student records temperature at 10 am over 5 days: Mon = 14°C, Tue = 16°C, Wed = 18°C, Thu = 17°C, Fri = 15°C. Plot these points and join with straight lines.

Example 5 — Stem-and-Leaf Diagram

The test scores are: 42, 45, 47, 51, 53, 53, 58, 61. Stem = tens, Leaf = units.

4 | 2 5 7
5 | 1 3 3 8
6 | 1
Key: 4 | 2 = 42

Example 6 — Histogram

The table shows times to run a race:

Class widths: 5 each. Frequency density = frequency ÷ width. - 0–4: 6 ÷ 5 = 1.2 - 5–9: 9 ÷ 5 = 1.8 - 10–14: 15 ÷ 5 = 3 Draw a histogram with bar heights equal to frequency densities.

Example 7 — Box Plot

Data summary: min = 22, Q1 = 34, median = 45, Q3 = 56, max = 70. Draw a box plot: - Box from 34 to 56, with a line at 45. - Whiskers from 22 to 70.

Example 8 — Scatter Graph

Hours studied vs test scores for 6 students: (1, 45), (2, 55), (3, 62), (4, 70), (5, 74), (6, 82). Plot points and note the positive correlation: more study hours → higher marks.

Example 9 — Frequency Polygon

Data: - Class 0–9, frequency 5 → midpoint 4.5 - Class 10–19, frequency 7 → midpoint 14.5 - Class 20–29, frequency 6 → midpoint 24.5 Plot (4.5, 5), (14.5, 7), (24.5, 6) and join with straight lines.

Worked Examples — Higher

At Higher tier, statistical plot questions often involve grouped data, estimation, and interpretation. Here are some examples with typical exam-style reasoning.

Example 1 — Histogram with unequal class widths

The times (minutes) taken by 40 students to finish a test are grouped:

Class widths: 10, 10, 20. Frequency densities: - 0–10 → \(8 ÷ 10 = 0.8\) - 10–20 → \(12 ÷ 10 = 1.2\) - 20–40 → \(20 ÷ 20 = 1.0\) Draw a histogram with these densities as bar heights.

Example 2 — Box Plot Comparison

Two classes take the same test:

• Class A: min = 15, Q1 = 32, median = 48, Q3 = 62, max = 75
• Class B: min = 20, Q1 = 40, median = 46, Q3 = 50, max = 60

Box plots show: - Class A has higher spread (range = 60, IQR = 30). - Class B is more consistent (range = 40, IQR = 10). Interpretation: Class A has some very high scorers but less consistency; Class B’s results are tightly clustered.

Example 3 — Scatter Graph with Line of Best Fit

Data: Hours studied vs exam score. Points: (2, 40), (4, 50), (6, 60), (8, 70), (10, 78).

Plot these and draw a line of best fit. Interpretation: strong positive correlation. Estimate: 7 hours study ≈ score of 65.

Example 4 — Pie Chart with percentages

A survey of 180 students records favourite sports: Football 72, Basketball 54, Tennis 36, Swimming 18. Total = 180. Angles: - Football = \(72 ÷ 180 × 360° = 144°\) - Basketball = \(54 ÷ 180 × 360° = 108°\) - Tennis = \(36 ÷ 180 × 360° = 72°\) - Swimming = \(18 ÷ 180 × 360° = 36°\) Draw the pie chart with labelled sectors.

Example 5 — Frequency Polygon with comparison

Two groups record times (minutes) for a challenge. Plot two frequency polygons on the same axes using midpoints. Interpretation: Group A peaks earlier, Group B takes longer on average. Comparing shapes gives insights into distributions.

Example 6 — Stem-and-Leaf Diagram (back-to-back)

Two classes’ scores: Class A = 32, 35, 37, 41, 44, 48 Class B = 31, 33, 36, 39, 42, 45

Class A | Stem | Class B
      2 | 3 | 1
     57 | 3 | 1369
    148 | 4 | 25

Interpretation: Medians are similar but Class A has slightly higher spread.

Common Mistakes & Fixes

Statistical plots are straightforward to draw, but small mistakes can cost easy marks. Here are the most common errors students make — and how to avoid them.

• Bar charts: forgetting gaps or uneven bar widths Mistake: Bars drawn with different widths or no gaps (confused with histogram). Fix: Keep all bars the same width with clear gaps between categories.
• Pictograms: unclear keys Mistake: Drawing symbols without explaining what they represent. Fix: Always add a key (e.g. 🍎 = 2 students).
• Pie charts: wrong angles Mistake: Dividing circle roughly instead of calculating angles. Fix: Use a protractor and the formula \(\text{Angle} = \frac{\text{Frequency}}{\text{Total}} \times 360°\).
• Line graphs: uneven scales Mistake: Axis numbers spaced irregularly, distorting trends. Fix: Choose an even, logical scale and stick to it.
• Stem-and-leaf diagrams: missing order or key Mistake: Leaves not in ascending order, or no key given. Fix: Sort leaves properly and write a key (e.g. 4 | 7 = 47).
• Histograms: treating like bar charts Mistake: Leaving gaps between bars or using frequency instead of frequency density. Fix: Bars touch, and height = frequency ÷ class width.
• Box plots: mixing up range and IQR Mistake: Saying the box length shows the range (it shows the interquartile range). Fix: Range = whiskers, IQR = box width.
• Scatter graphs: forcing a line through all points Mistake: Joining points like a line graph instead of a line of best fit. Fix: Draw a smooth line of best fit that balances points above and below.
• Frequency polygons: forgetting to close ends Mistake: Leaving ends “floating”. Fix: Extend back to the x-axis at the start and end (optional but clearer).
• General: missing labels and units Mistake: Drawing a perfect graph but not labelling axes. Fix: Always write axis titles, units, and keys where needed.

Practice Questions — Foundation

These practice problems focus on straightforward construction and interpretation of statistical plots. Use squared paper or graph paper where possible, and remember to label axes and include units.

1. A survey asked 20 students their favourite subject: Maths = 7, English = 5, Science = 6, History = 2.
   Draw a bar chart of the results.
2. A pictogram shows pets owned by families. Each symbol 🐶 = 2 pets.
   Complete the pictogram if the data are: Dogs = 6, Cats = 8, Rabbits = 4.
3. A class of 24 students records how they travel to school: Walk = 12, Bus = 6, Car = 4, Cycle = 2.
   Draw a pie chart of the data.
4. The rainfall (mm) over 5 days was: Mon = 2, Tue = 4, Wed = 0, Thu = 3, Fri = 5.
   Plot a line graph of rainfall against day of the week.
5. Test scores: 34, 36, 42, 45, 47, 48, 50, 51.
   Draw a stem-and-leaf diagram. Add a key.
6. A frequency table of shoe sizes:

   Shoe size	Frequency
   3–4	5
   5–6	8
   7–8	7

   Draw a histogram for this data (equal class widths).
7. The following summary is given for exam marks: Min = 22, Q1 = 30, Median = 38, Q3 = 46, Max = 60.
   Draw a box plot.
8. Heights (cm) of 5 children: 98, 104, 100, 96, 102.
   Calculate the range and show how it relates to the box plot.
9. The points (1, 2), (2, 3), (3, 4), (4, 5) show relationship between study hours and scores.
   Plot a scatter graph and describe the correlation.
10. Class intervals of time (minutes) for a task: 0–9 → 4 students, 10–19 → 6 students, 20–29 → 5 students.
    Draw a frequency polygon using class midpoints.

Practice Questions — Higher

These questions are more challenging and often combine drawing skills with interpretation. Work carefully through calculations (like angles and frequency density) and always explain your answers in context.

1. A survey of 180 people asked about their favourite sport: Football = 72, Basketball = 54, Tennis = 36, Swimming = 18.
   (a) Draw a pie chart. (b) State the angle for the Basketball sector.
2. A frequency table of times to complete a quiz:

   Time (mins)	Frequency
   0–5	5
   5–15	15
   15–25	20
   25–40	10

   (a) Calculate frequency density for each class. (b) Draw a histogram. (c) Comment on the most common completion times.
3. A box plot shows the following for Class A: min = 18, Q1 = 32, median = 40, Q3 = 56, max = 70. Another box plot for Class B: min = 22, Q1 = 35, median = 42, Q3 = 46, max = 55.
   (a) State the range and IQR for each class. (b) Compare the two distributions in terms of consistency and typical performance.
4. A scatter graph plots students’ revision hours against test scores. The line of best fit is drawn as \( y = 5x + 40 \).
   (a) Use the line to estimate the score for 7 hours of revision. (b) Comment on the strength and type of correlation.
5. Two sets of grouped data are plotted on the same frequency polygon. Group A peaks at midpoint 15 with frequency 12; Group B peaks at midpoint 25 with frequency 14.
   (a) Sketch both polygons on one graph. (b) Compare the distributions.
6. The weights (kg) of parcels are shown in a stem-and-leaf diagram:

       Stem | Leaf
         4  | 2 5 7
         5  | 1 3 3 8
         6  | 0 2 4
       

   (a) Write out the raw data. (b) Find the median and the range. (c) Explain one advantage of using a stem-and-leaf over a bar chart.
7. A grouped frequency table shows student shoe sizes:

   Shoe size	Frequency
   3–4	2
   5–6	10
   7–8	8
   9–11	5

   (a) Estimate the mean shoe size using midpoints. (b) Draw a histogram. (c) Suggest why the range might be misleading in this case.

Challenge Questions

These multi-step problems combine calculations, drawing, and interpretation. They are designed to stretch your understanding and prepare you for the most demanding GCSE exam questions.

1. A company records daily sales (£) of a product for 40 days, summarised in a histogram: - 0–50: frequency 6 - 50–100: frequency 14 - 100–200: frequency 20
   (a) Calculate frequency densities. (b) Draw the histogram. (c) State whether most sales were under or over £100, giving a reason.
2. Two box plots compare heights of boys and girls in a class. Boys: min = 130, Q1 = 140, median = 150, Q3 = 160, max = 175. Girls: min = 132, Q1 = 145, median = 155, Q3 = 162, max = 168.
   (a) Find the range and IQR for each group. (b) Compare height distributions between boys and girls.
3. A scatter graph shows weekly revision hours vs exam scores. The correlation is strongly positive. (a) Explain why correlation does not prove causation. (b) Suggest one other factor that could affect exam scores.
4. A frequency polygon compares weights of apples from two farms. Farm A’s polygon peaks at midpoint 120 g, Farm B’s at 150 g. (a) Sketch the polygons. (b) State which farm produces heavier apples on average and which has greater consistency.
5. A pie chart represents transport methods of 200 commuters: Car = 100, Bus = 50, Train = 30, Cycle = 20. (a) Calculate each sector angle. (b) Draw the pie chart. (c) If the number of car users falls by 20, redraw the chart to show new proportions.
6. The following back-to-back stem-and-leaf diagram shows exam scores for two classes:

       Class A | Stem | Class B
           1 2 | 4 | 3 8
         4 5 9 | 5 | 0 3 7
           0 3 | 6 | 1 5
       

   (a) Write out the data sets. (b) Find the median and range for each class. (c) Compare the performance of the two classes.
7. A grouped table shows the times (minutes) taken for 60 runners:

   Time (mins)	Frequency
   20–29	10
   30–39	20
   40–59	25
   60–79	5

   (a) Calculate midpoints and plot a frequency polygon. (b) Estimate the mean time. (c) Suggest one reason why the polygon is better than a bar chart here.

Quick Revision Sheet

Here is a one-page summary of the main GCSE statistical plots — perfect for last-minute exam revision.

Bar Chart

• Used for categories (discrete data).
• Equal-width bars, gaps between them.
• Height = frequency.

Pictogram

• Uses symbols/icons for frequency.
• Always include a clear key (e.g. 1 symbol = 5 people).

Pie Chart

• Shows proportions of a whole (out of 360°).
• Angle = \(\frac{\text{Category}}{\text{Total}} \times 360°\).

Line Graph

• Shows continuous change (often over time).
• Plot points, join with straight lines.

Stem-and-Leaf Diagram

• Keeps raw data visible in ordered form.
• Add a key (e.g. 4 | 7 = 47).

Histogram

• For grouped continuous data.
• Bar height = frequency density = frequency ÷ class width.
• No gaps between bars.

Box Plot

• Shows 5 key values: min, Q1, median, Q3, max.
• Range = max − min, IQR = Q3 − Q1.
• Good for comparing distributions.

Scatter Graph

• Plots pairs of values (x, y).
• Look for correlation (positive, negative, none).
• Line of best fit used to estimate values.

Frequency Polygon

• Plots class midpoints against frequency.
• Join points with straight lines.
• Useful for comparing two distributions.

General Exam Tips

• Label all axes, units, and keys clearly.
• Choose neat, even scales.
• Always interpret graphs in context (spread, consistency, correlation).

Answers

Foundation

1. Bar chart: 4 bars labelled Maths (7), English (5), Science (6), History (2).
2. Pictogram: - Dogs = 3 symbols - Cats = 4 symbols - Rabbits = 2 symbols Key: 1 🐶 = 2 pets.
3. Pie chart angles: - Walk: \( \tfrac{12}{24} \times 360° = 180° \) - Bus: \( \tfrac{6}{24} \times 360° = 90° \) - Car: \( \tfrac{4}{24} \times 360° = 60° \) - Cycle: \( \tfrac{2}{24} \times 360° = 30° \).
4. Line graph: Points (Mon, 2), (Tue, 4), (Wed, 0), (Thu, 3), (Fri, 5). Trend: fluctuating rainfall, highest on Friday.
5. Stem-and-leaf:

       3 | 4 6
       4 | 2 5 7 8
       5 | 0 1
       Key: 4 | 2 = 42
       

6. Histogram: class width = 2. Frequencies: 5, 8, 7. Frequency densities = 2.5, 4, 3.5.
7. Box plot values: min = 22, Q1 = 30, median = 38, Q3 = 46, max = 60.
8. Range: \( 102 − 96 = 6 \). This is shown as the whisker length on a box plot.
9. Scatter graph shows positive correlation. More study hours → higher scores.
10. Frequency polygon points: (4.5, 4), (14.5, 6), (24.5, 5). Joined with straight lines.

Higher

1. Pie chart: - Football: 144° - Basketball: 108° - Tennis: 72° - Swimming: 36° (b) Basketball = 108°.
2. Frequency densities: - 0–5 → 1.0 - 5–15 → 1.5 - 15–25 → 2.0 - 25–40 → 0.67 Histogram drawn. Most students finished between 15–25 mins.
3. Class A: range = 55, IQR = 30. Class B: range = 33, IQR = 11. Comparison: Class A more spread, Class B more consistent but lower maximum.
4. Line of best fit: \( y = 5x + 40 \). For 7 hours: \( y = 5 \times 7 + 40 = 75 \). Correlation: strong positive.
5. Two frequency polygons: Group A peaks earlier at 15 mins, Group B later at 25 mins. Interpretation: Group B takes longer on average.
6. Raw data: 42, 45, 47, 51, 53, 53, 58, 60, 62, 64. Median = 53, Range = 22. Advantage: Stem-and-leaf keeps raw data visible.
7. Midpoints: 3.5, 5.5, 7.5, 10. Estimated mean = \(\tfrac{(3.5 \times 2) + (5.5 \times 10) + (7.5 \times 8) + (10 \times 5)}{25}\) = \(\tfrac{7 + 55 + 60 + 50}{25} = \tfrac{172}{25} = 6.88\). Histogram drawn. Range misleading: large spread due to few extreme values in 9–11 class.

Challenge

1. Frequency densities: - 0–50: \(6 ÷ 50 = 0.12\) - 50–100: \(14 ÷ 50 = 0.28\) - 100–200: \(20 ÷ 100 = 0.20\). Most sales > £100 (largest class).
2. Boys: range = 45, IQR = 28. Girls: range = 36, IQR = 17. Comparison: Girls more consistent, slightly taller on average.
3. Correlation ≠ causation. Other factors: teaching quality, exam technique, stress, etc.
4. Polygons show Farm B heavier on average, Farm A more consistent.
5. Angles: Car = 180°, Bus = 90°, Train = 60°, Cycle = 30°. After reduction: Car = 80, new total = 180. New angles: Car = 160°, Bus = 90°, Train = 60°, Cycle = 40°.
6. Class A data: 41, 42, 45, 49, 50, 53. Class B data: 43, 48, 50, 53, 57, 61, 65. Class A median ≈ 46, range = 12. Class B median ≈ 53, range = 22. Comparison: Class B scores higher but less consistent.
7. Midpoints: 24.5, 34.5, 49.5, 69.5. Frequencies: 10, 20, 25, 5. Estimated mean = \(\tfrac{(24.5 \times 10) + (34.5 \times 20) + (49.5 \times 25) + (69.5 \times 5)}{60}\) = \(\tfrac{245 + 690 + 1237.5 + 347.5}{60} = \tfrac{2520}{60} = 42\). Frequency polygon smoother than bar chart → easier to compare shapes.

Conclusion & Next Steps

Statistical plots are the language of data. They transform raw numbers into pictures that reveal patterns, comparisons, and relationships. At GCSE level, you need to be confident in both constructing and interpreting a wide range of plots, from simple bar charts to more advanced histograms and box plots.

• What you’ve mastered: how to draw bar charts, pie charts, line graphs, histograms, box plots, scatter graphs, and more — with the correct scales, labels, and keys.
• How to avoid mistakes: by remembering rules (e.g. histograms = frequency density, box length = IQR, scatter graphs need line of best fit).
• Interpretation skills: describing centre (mean/median), spread (range/IQR), and correlation for full marks in Higher-tier questions.

Plots are not just exam tools. They are used in everyday life — from sports statistics and weather charts to financial graphs and medical studies. Learning to read them critically helps you make better sense of the world around you.

Where this connects next:

1. Averages (mean, median, mode): Many plots are used alongside averages to summarise data sets fully.
2. Probability distributions: Histograms and frequency polygons are stepping stones to probability and normal distribution curves.
3. Comparing data: Box plots, histograms, and scatter graphs are designed for comparing two or more sets of results.
4. Real-world applications: Plots are essential in business, science, medicine, and government for making data-driven decisions.