7.2 Data Presentation for Research Questions | Digital Literacy | Computer Science 11

Once data has been collected, it must be presented effectively so that it can answer the original research question. Good data presentation transforms raw numbers into meaningful insights using summary statistics and visual tools.

What is Data Presentation?

Data presentation is the process of organizing and displaying data in a structured format — using tables, charts, graphs, or statistics — so that patterns, trends, and conclusions become clear.

Key principle: Data must be presented in a clear, accurate, and unbiased manner to support valid research conclusions.

Summary Statistics

Summary statistics are numerical values that describe the main features of a data set. They allow a researcher to quickly understand the data without examining every individual value.

Statistic	Description	Example (data: 3, 5, 5, 7, 10)
Mean	Sum of all values ÷ number of values	(3+5+5+7+10) ÷ 5 = 6
Median	Middle value when data is sorted	5
Mode	Most frequently occurring value	5
Range	Maximum value − Minimum value	10 − 3 = 7

Data Visualization

Data visualization is the graphical representation of data using visual elements such as charts, graphs, and maps. It makes it easier to identify trends, outliers, and patterns that are not obvious in raw data.

Features of Effective Data Visualization

Clarity — Easy to read and understand at a glance.
Accuracy — Scales, proportions, and values are represented truthfully.
Efficiency — Conveys complex information quickly without unnecessary clutter.

Types of Data Visuals

1. Bar Chart

Used to compare quantities across different categories.
The height (or length) of each bar represents the value for that category.
Example: Comparing the number of students who scored in each grade range.

2. Pie Chart

Used to show proportions or percentages of a whole.
Each slice represents a category's share of the total.
Example: Showing the percentage of students preferring different subjects.
⚠️ Best used when there are fewer than 6 categories.

3. Line Graph

Used to show trends over time (continuous data).
Points are connected by a line to show direction of change.
Example: Tracking a student's test scores over five months.

Choosing the Right Chart

Situation	Best Chart
Comparing categories	Bar Chart
Showing parts of a whole	Pie Chart
Showing change over time	Line Graph

Experimental Design in Data Science

Before collecting and presenting data, a researcher must plan the experimental design — the structured approach to answering a research question.

Key Components of Experimental Design

Component	Description	Example
Research Question	The question the study aims to answer	Does study time affect exam scores?
Hypothesis	A testable prediction	More study hours → higher scores
Independent Variable	The variable that is deliberately changed	Number of study hours
Dependent Variable	The variable that is measured	Exam scores
Control Variables	Variables kept constant to ensure fairness	Same subject, same difficulty level
Data Collection Method	How data will be gathered	Survey, observation, experiment

Why Experimental Design Matters

A well-designed experiment ensures that:

Results are reliable and can be repeated.
Conclusions are valid and not due to uncontrolled factors.
Data presentation accurately reflects the research question.

Connecting Data Presentation to Research Questions

The choice of how to present data should always be guided by the research question:

If the question asks "How many?" → use a bar chart or table.
If the question asks "What proportion?" → use a pie chart.
If the question asks "How has this changed over time?" → use a line graph.
If the question asks "What is the average?" → calculate and report the mean.

What is Data Presentation?

Data presentation is the process of organizing and displaying data in a structured format — using tables, charts, graphs, or statistics — so that patterns, trends, and conclusions become clear.

Key principle: Data must be presented in a clear, accurate, and unbiased manner to support valid research conclusions.

Summary Statistics

Summary statistics are numerical values that describe the main features of a data set. They allow a researcher to quickly understand the data without examining every individual value.

Statistic	Description	Example (data: 3, 5, 5, 7, 10)
Mean	Sum of all values ÷ number of values	(3+5+5+7+10) ÷ 5 = 6
Median	Middle value when data is sorted	5
Mode	Most frequently occurring value	5
Range	Maximum value − Minimum value	10 − 3 = 7

Data Visualization

Features of Effective Data Visualization

Clarity — Easy to read and understand at a glance.
Accuracy — Scales, proportions, and values are represented truthfully.
Efficiency — Conveys complex information quickly without unnecessary clutter.

Types of Data Visuals

1. Bar Chart

Used to compare quantities across different categories.
The height (or length) of each bar represents the value for that category.
Example: Comparing the number of students who scored in each grade range.

2. Pie Chart

Used to show proportions or percentages of a whole.
Each slice represents a category's share of the total.
Example: Showing the percentage of students preferring different subjects.
⚠️ Best used when there are fewer than 6 categories.

3. Line Graph

Used to show trends over time (continuous data).
Points are connected by a line to show direction of change.
Example: Tracking a student's test scores over five months.

Choosing the Right Chart

Situation	Best Chart
Comparing categories	Bar Chart
Showing parts of a whole	Pie Chart
Showing change over time	Line Graph

Experimental Design in Data Science

Before collecting and presenting data, a researcher must plan the experimental design — the structured approach to answering a research question.

Key Components of Experimental Design

Component	Description	Example
Research Question	The question the study aims to answer	Does study time affect exam scores?
Hypothesis	A testable prediction	More study hours → higher scores
Independent Variable	The variable that is deliberately changed	Number of study hours
Dependent Variable	The variable that is measured	Exam scores
Control Variables	Variables kept constant to ensure fairness	Same subject, same difficulty level
Data Collection Method	How data will be gathered	Survey, observation, experiment

Why Experimental Design Matters

A well-designed experiment ensures that:

Results are reliable and can be repeated.
Conclusions are valid and not due to uncontrolled factors.
Data presentation accurately reflects the research question.

Connecting Data Presentation to Research Questions

The choice of how to present data should always be guided by the research question:

If the question asks "How many?" → use a bar chart or table.
If the question asks "What proportion?" → use a pie chart.
If the question asks "How has this changed over time?" → use a line graph.
If the question asks "What is the average?" → calculate and report the mean.