Statistics for Beginners

Statistics for Beginners on Breni teaches how to describe data, reason about chance and draw sound conclusions from samples. Its 8 courses cover what statistics is, summarising data, charts, probability, distributions, sampling and confidence intervals, hypothesis testing, and regression. Learners finish able to calculate a standard deviation, build a confidence interval, run a t-test and read a p-value correctly. Only school-level algebra is assumed. Probability comes before inference because every test relies on it, and each course becomes available once the last is done.

  • 8 courses
  • 15 learning objectives
  • Level: beginner
  • Language: English
  • Price: Subscription, free trial

What you will be able to do

  • Calculate the mean, median and standard deviation of a dataset
  • Read histograms, box plots and scatter plots and spot misleading charts
  • Calculate conditional probabilities with a tree diagram
  • Use z-scores and the normal distribution to find probabilities
  • Build a 95% confidence interval and run a t-test
  • Fit a simple linear regression and explain what R-squared shows

Full curriculum

Part 1 · Describing data

1. Statistics: What It Is and How It Works

Explain what statistics is for and the kinds of data it works with. Units, in order: Populations and samples; Types of data; Descriptive and inferential statistics; How statistics can mislead; Collecting data well.

  • — Populations and Samples
    Learn the difference between whole groups and small groups.
    • What Is a Population
    • What Is a Sample
    • Parameters and Statistics
  • — Types of Data
    Discover the different kinds of information numbers can show.
    • Qualitative Data
    • Quantitative Data
    • Data Levels
  • — Descriptive and Inferential Statistics
    Learn how to summarize data and guess wider truths.
    • Descriptive Statistics Basics
    • Inferential Statistics Basics
    • Using Both Methods
  • — How Statistics Can Mislead
    Spot common tricks and errors in numerical claims.
    • Biased Samples
    • Correlation and Causation
    • Misleading Charts
  • — Collecting Data Well
    Build good habits to gather clean and honest data.
    • Random Sampling
    • Designing Good Surveys
    • Avoiding Data Errors

2. Summarising Data

Describe a dataset with averages and measures of spread. Units, in order: Mean, median and mode; Range and interquartile range; Variance and standard deviation; Percentiles and quartiles; Spotting and handling outliers.

3. Charts for Statistics

Choose and read charts that show a dataset clearly. Units, in order: Histograms and the shape of a distribution; Box plots and comparing groups; Scatter plots and relationships; Bar and line charts; Misleading graphs.

Part 2 · Probability

4. Probability Basics

Calculate probabilities of single and combined events. Units, in order: The addition and multiplication rules; Independent and dependent events; Conditional probability; Tree diagrams; Expected value and fair games.

5. Probability Distributions

Use the binomial and normal distributions. Units, in order: Discrete and continuous random variables; The binomial distribution; The normal distribution; Standardising values with z-scores; The central limit theorem.

Part 3 · Inference

6. Sampling and Confidence Intervals

Estimate a population value from a sample. Units, in order: Sampling methods; Sampling error and bias; Standard error of the mean; Confidence intervals; Choosing a sample size.

7. Hypothesis Testing

Test claims with data and read p-values correctly. Units, in order: Null and alternative hypotheses; p-values and significance; One-sample and two-sample t-tests; Chi-square tests for categories; Type I and Type II errors.

8. Correlation and Regression

Measure relationships between variables and make predictions. Units, in order: Correlation coefficients; Correlation and causation; Simple linear regression; Interpreting R-squared; Multiple regression in brief; Residuals and checking a regression fits.

Sample: Statistics: What It Is and How It Works

The opening tier, (Populations and Samples). Learn the difference between whole groups and small groups.

What Is a Population

A population is every person or item in your study group.

What Is a Sample

A sample is a smaller group chosen from the whole population.

Parameters and Statistics

Parameters describe populations while statistics describe samples.

Questions about Statistics for Beginners

Is statistics hard to learn?

It is easier when each idea builds on the last. Statistics for Beginners on Breni starts with averages and charts before probability and tests.

How much maths do I need for statistics?

School-level algebra is enough. The path explains each formula with a worked example.

What is the difference between descriptive and inferential statistics?

Descriptive statistics summarise the data you have. Inferential statistics use a sample to draw conclusions about a larger population.

Does this path help with data science?

Yes. The ideas here, especially distributions, tests and regression, underpin data science and machine learning.

Do I get a certificate in statistics?

Completing all 8 courses earns a Breni certificate of completion.

Related learning paths

Start Statistics for Beginners

Get Breni on Google Play and start Statistics for Beginners Download on the App Store