How to Learn Statistics in 2024: A Complete Guide for Beginners

Core Statistics Concepts to Understand

Building a strong conceptual foundation is critical before you get your hands on real-life projects. So, if you’re completely new to statistics, start by focusing on the core concepts I mention below: 

Descriptive statistics

With descriptive statistics, you can summarize and describe the main features of a dataset in both numerical and visual forms. The average of a sample is an example of descriptive statistics, which is further divided into the following: 

Measures of central tendency 

They describe where the most data lies. There are three ways to calculate it: 

Measures of variability 

They show how spread out the data points are from the mean, including range, variance, and standard deviation.  

Correlation 

Correlation measures the strength and direction of the relationship between two variables. Here's what different correlations mean: 

• A correlation of 0 signifies there is no linear relationship.
• +1 indicates a perfect relationship. If one variable increases, the other also increases. 
• -1 shows that there's a negative relationship. This means that if one variable increases, the other decreases. 

Check out this descriptive Statistics Cheat Sheet for an in-depth overview.  

Probability

Probability measures uncertainty to assess how likely an event is to occur based on a ratio of favorable and possible outcomes. For a more accurate estimate, you should increase the number of trials. Basic probability rules include: 

• Range of probabilities: Probabilities range from 0 to 1, where 0 shows that the event will not happen, and 1 means that the event will occur. 
• The sum of probabilities: The total probability of all possible outcomes equals 1. 
• Addition rule: If two events cannot happen simultaneously, their total probability equals the addition of individual probabilities. 

P(A or B) = P(A) + P(B) 

If two events can occur together, subtract the probability of both occurring simultaneously. 

P(A or B) = P(A) +P (B) - P(A and B) 

• Multiplication rule: The probability of both independent events happening is the product of their probabilities. 

P(A and B) = P(A) × P(B) 

In the independent events, the probability of one event happening is given that another event already occurred. 

P(A and B) = P(A) × P(B/A) 

Conditional probability 

It measures the likelihood of an event when there's a condition attached. Data scientists use this probability type to tackle “if-then” questions. For example, you can use it to determine if buying a specific stock will improve a client's portfolio. 

Probability distributions 

Probability distributions describe how probabilities are distributed over possible outcomes. For example, you may want to predict the success rate of a marketing campaign based on these factors:

• Budget 
• Demographics 
• Campaign duration 

Since you don't know how these factors affect success, you can use probability distribution to make informed decisions based on past data. 

Inferential statistics 

Inferential statistics allows you to make conclusions about a larger population based on observations from a sample. It involves two potential errors or uncertainties:

• Type I error occurs when you mistakenly reject a true null hypothesis. 
• Type II error indicates that you cannot reject a false null hypothesis. 

Hypothesis testing 

This testing is an essential form of inferential statistics, which allows you to test an assumption about a population based on sample data. There are two hypotheses: 

• The null hypothesis assumes no effect or difference. 
• An alternative hypothesis is always the opposite of the null hypothesis. 

After setting these hypotheses, you choose a significance level, often 0.05. A “p-value” less than the significance level rejects the null hypothesis. Otherwise, you accept it. 

Confidence intervals 

In confidence intervals, you can see a range of values that includes the true population parameter. For instance, a 95% confidence interval means if the study were repeated many times, the true value would fall within the intervals in 95% of those studies. 

Drawing conclusions is the core of data inferential statistics. By doing so, you can:

• Summarize key findings.
• Assess whether your hypothesis holds up.
• See how well your results apply to the population. 

Common statistical tests

Statistical tests analyze quantitative data and answer specific research questions. The most common tests include t-tests, chi-square tests, and ANOVA. Here’s a table that defines each test and lists their uses: 

I included this section to give a hint about statistics and probability. Now, let’s review how to learn them as effectively as possible! 

How to Learn Statistics from Scratch

Statistics covers a lot, which can be confusing for beginners. That’s why I’ve prepared a step-wise guide on how to start from scratch, choose the right topics, and find projects to equip you in this area fully: 

Step 1: Start with the basics

Before going into advanced topics, it's so important to brush up on basics such as descriptive statistics and probability: 

• Descriptive statistics: A solid grasp of descriptive statistics, including central tendency and variability measures, will allow you to present data meaningfully. 
• Probability: With basic knowledge of probability, you can reduce sampling bias and save money as you don't need to test the entire population. 

Here are some resources that provide more advanced knowledge on these topics: 

• Introduction to Probability Rules Cheat Sheet
• Introduction to Statistics
• Introduction to Statistics in Python

Step 2: Practice with real data

Understanding statistical concepts is not enough — you should use real datasets for practice, along with tools like Excel, Google Sheets, R, and Python. Here's how you can practice with these tools: 

• Google Sheets allows you to calculate the mean, create visualizations, and run linear regressions. 
• R allows you to perform different statistical analyses and represent data in graphics. 
• Python has different libraries to compute various statistics, including descriptive statistics and hypothesis testing. 

Learn more about these tools in the resources below: 

• Introduction to Statistics in Google Sheets
• Hypothesis Testing in R
• Statistical Thinking in Python

Step 3: Learn inferential statistics

Move to inferential statistics once you've understood the basic statistical concepts and know their practical implementation. This will help you analyze and interpret the data. You can use hypothesis testing, confidence intervals, and other relevant tests to compare two sample groups from a population. 

Check out this course to learn more about different inferential statistics concepts: 

• Statistical Inference with R
• Foundations of Inference in Python

Step 4: Explore advanced topics

Now it's time to challenge yourself a bit more. As you get comfortable with fundamental concepts, explore some meatier topics. Here are a few areas to look into:

• Regression analysis: It will help you understand relationships between different factors. It's super useful in fields like economics and social sciences.
• Time series analysis: If you're into predicting trends or working with data that changes over time, this one's for you. It's big in finance and weather forecasting.
• Bayesian statistics: This approach helps you update your beliefs as you get new information. It's convenient for decision-making and machine learning.

Don't worry if these sound complicated — take them one at a time. They will open up new ways of looking at data and solving problems. 

At this stage, check out these resources:

• Introduction to Regression in R course
• Introduction to Linear Modeling in Python course
• Time Series Analysis in Python course

Step 5: Apply statistics to projects

Do you remember that practice makes perfect rule? The same applies to statistics, too. The more you use them in real situations, the better you'll get at it. And it's a great way to understand how all those concepts work in the real world. 

So, if you want to get better at data analysis, work on some real projects. You can find different datasets on GitHub or Kaggle to perform your desired tests. After that, you can do regression analysis to analyze data and draw conclusions. 

Also, here are some of my recommended guided projects on statistics and data analysis (they include datasets): 

• Hypothesis Testing with Men’s and Women’s soccer matches
• Will This Customer Purchase Your Product?
• What Is Your Heart Rate Telling You?

An Example Learning Plan for Statistics 

Now you know how to go step by step, so it’s time to start. I’ve prepared this weekly learning plan to help you kickstart with basic concepts like descriptive statistics and probability and then move on to inferential statistics and various tests. 

Week 1: Introduction to descriptive statistics 

Start with the basics of descriptive statistics. Understanding these concepts is essential for cleaning and analyzing data effectively. Begin with simple datasets to practice measures like mean, median, mode, and standard deviation. It will help you learn how these statistics summarize and interpret data. 

Week 2: Understanding probability

Next, learn about the probability concepts. Calculate the likelihood of an event happening and get familiar with fundamental rules, like how to add and multiply probabilities. Also, learn how conditional probability changes the probability of one event based on another event. 

Study common probability distributions to understand how data is spread out. Practice with real-life examples, such as calculating the probability of rolling various numbers on a dice. 

Week 3: Inferential statistics basics

Learn the basics of hypothesis testing. They help in fields like clinical trials to determine if a new treatment works better than the existing ones. 

Next, understand the concepts of confidence intervals and p-values. They are essential if you want to make informed decisions about data. Practicing these concepts on datasets like healthcare studies or sports statistics will allow you to draw conclusions. 

Week 4: Exploring statistical tests 

This week, learn different statistical tests, including t-tests and chi-square tests. Understand how to use these tests on different data types. For instance, a t-test can be used to compare the averages of two groups, and a chi-square test can be run to find the relationship between two variables. This will help you compare survey results and understand patterns in customer behavior.  

Week 5: Advanced topics and applications

At last, focus on advanced techniques like regression analysis, ANOVA, and time series analysis. It will help you find relations between different variables. For instance, you can use regression analysis to show how advertisements impact sales. ANOVA will help you compare different campaigns. For a better understanding, you should apply these techniques to complex datasets.  

Best Resources for Learning Statistics

If you want to build a strong foundation in statistics, you have to get your hands on the best available resources. Here are some of my top picks for you: 

Online courses

DataCamp offers some beginner-friendly courses if you want to start learning statistics online. You can use our interactive format and hands-on exercises to grasp concepts more effectively. 

Here are some of my recommendations for you:

• For statistics basics: Introduction to Statistics
• For learning statistics in R: Introduction to Statistics in R
• Use Python for hypothesis testing: Hypothesis Testing in Python
• For learning regression techniques in Python: Introduction to Regression with statsmodels in Python
• Learn how to use Google Sheets for statistics: Introduction to Statistics in Google Sheets
• Learn how to use Tableau for statistics: Statistical Techniques in Tableau  

Read books 

If you prefer deep dives into subjects, there's nothing better than books. Check out these books for strong theory knowledge, along with relatable examples. 

• An essential read for statistics: Naked Statistics by Charles Wheelan
• For an advanced guide on statistics: The Elements of Statistical Learning by Hastie, Tibshirani, and Friedman 

YouTube channels and tutorials

For those who prefer free video lessons, YouTube has great channels and tutorials. Here are some of my recommendations for you: 

• Khan Academy has a whole playlist on statistics covering all the major topics.
• StartQuest with Josh Starmer is another channel that has playlists on statistics fundamentals and statistics in R. 

Practice platforms

DataCamp offers a lot of projects where you can apply statistical analysis to real-world datasets. The best part is that you can add those projects to your portfolio. If you want to go the extra mile, try Kaggle — another platform that provides datasets and allows you to challenge your skills. 

Check out the following resources:  

• For data visualization: A Visual History of Nobel Prize Winners
• For applying your data cleaning and data manipulation skills: Exploring Airbnb Market Trends
• To perform k-means cluster analysis: Degrees That Pay You Back

Tips for Mastering Statistics

I've been through the statistics learning journey myself, and I want to share some tips that helped me. These strategies can make a big difference in how well you perceive and apply statistical concepts. Here's how to go with it: 

• Practice regularly: If you want to become a data scientist and can't perform simple tests like chi-square, you need to practice more frequently. Work on real problems and apply statistics in real-world scenarios. This will solidify your knowledge, and you’ll be able to think more critically. 
• Join study groups or forums: Statistics is a broader field, and learning it independently can be challenging. There are online communities, such as forums and groups, where you can ask queries. So, it’s best to join such communities and collaborate with fellow analysts. 
• Work on real projects: Apply your knowledge to actual projects. For example, you could analyze data for a research paper or conduct market research. I would even suggest participating in a few data science competitions - they will be challenging but can boost your skills. 
• Stay curious and keep learning: Since technology keeps evolving, the hiring criteria also do not remain the same. Some companies require basic statistics knowledge, while others prefer in-depth understanding. So, you must stay updated about different statistical tools and their applications. 

Final Thoughts 

Learning statistics can be overwhelming if you're not into mathematics and just starting your career in data science. With the right approach and resources, you can simplify your journey to master statistics. As new tools emerge for complex data analysis, stay updated with the latest developments. 

R is the most common language that provides built-in functions for various statistical tests. If you don't know how to use R for statistics, check out our complete career guide, Statistician with R.