Explanation 1
Data are recorded observations, such as wait times, device types, or survey responses.
Loading
Loading
Lesson 1 of 9
Statistics helps people ask better questions, collect relevant data, and make careful decisions under uncertainty.
Theory
Statistics is the practice of learning from data. It helps us decide what to measure, how to organize what we measured, how to summarize patterns, and how to communicate what those patterns suggest. A single number rarely tells the whole story. Statistical thinking asks where the number came from, how much it varies, and what decision it can responsibly support.
Explanation 1
Data are recorded observations, such as wait times, device types, or survey responses.
Explanation 2
Information is data that has been organized so someone can use it.
Explanation 3
A statistic is one calculated value from data; statistics is the wider discipline of working with data.
Terminology
Raw recorded values before interpretation.
Customer wait times: 41 seconds, 66 seconds, 52 seconds.
Data shaped into a useful message.
Most customers waited under one minute, but a few waited much longer.
A numerical summary calculated from observed data.
The average wait time for 80 observed customers is 58 seconds.
The full process of collecting, analyzing, interpreting, and communicating data.
Designing a wait-time study and recommending a checkout staffing change.
Visual
A useful analysis moves through a pipeline: collect data, organize it, analyze it, interpret it, and communicate the result.
Collect
Organize
Analyze
Interpret
Communicate
Interactive visual
Select each step to see how a raw business question becomes a clear statistical message.
Collect
Example: record daily website visits, signups, device type, and bounce rate.
Accessible chart summary: The pipeline shows five ordered steps: collect, organize, analyze, interpret, and communicate. The highlighted step changes when a student selects a stage.
Notation and formulas
sample statistic = summary calculated from observed data
The exact formula depends on the question. A mean summarizes typical size, a proportion summarizes a share, and a count summarizes how often something happened.
Worked example
Scenario
An online store wants to know whether customers wait too long for chat support. The raw data are individual wait times. The information might be a dashboard showing the median wait, long-delay count, and busiest hour. The decision might be whether to add staff during afternoon peaks.
The same dataset can support different summaries, so the decision question should guide the statistic.
Why variation matters
If every customer waited exactly 58 seconds, planning would be simple. Real wait times vary. Variation tells us whether most experiences are similar or whether a few customers face serious delays.
Reflection
Assistant
Give me a new example of data, information, a statistic, and a decision using a small business scenario.
Exit check
Test the difference between raw data, a statistic, and a decision.
Checkpoint
Question 1 of 5. Answered 0/5. Passing score: 70%.