Statistics for Non-Statisticians: Making Sense of Data

Population vs. Sample

Population: The entire group you want to draw conclusions about.

Sample: A subset of the population that you actually examine.

Why it matters: We often can’t study entire populations, so we use samples to make inferences.

Variables

Categorical Variables: Data that falls into categories (e.g., color, gender).

Numerical Variables: Data that can be measured or counted.

• Discrete: Whole numbers (e.g., number of children)
• Continuous: Any value within a range (e.g., height, weight)

Measures of Central Tendency

Measures of Spread

Key Probability Concepts

• Independent Events: The occurrence of one event doesn’t affect the probability of another.
• Dependent Events: The probability of an event changes based on the occurrence of another event.
• Mutually Exclusive Events: Events that cannot occur simultaneously.

Common Chart Types

Key Terms

• Null Hypothesis (H₀): The default assumption of no effect or no difference.
• Alternative Hypothesis (H₁ or Ha): The hypothesis you’re testing for.
• p-value: The probability of obtaining results at least as extreme as the observed results, assuming the null hypothesis is true.
• Significance Level (α): The threshold below which you reject the null hypothesis (commonly 0.

  This article will show you how to grasp and use statistical ideas, even if you’re not a pro. We’ll cover how to get better at statistics, read data visualizations, and talk to statisticians. By the end, you’ll know how to use data to your advantage and make choices based on facts.

  Key Takeaways

  • Statistics is a powerful tool for analyzing data and making evidence-based decisions²
  • Understanding statistical concepts can help non-statisticians make sense of their data
  • Improving numeracy skills and getting comfortable with numbers is a key step
  • Mastering linear models can provide a versatile tool for data analysis
  • Effective communication with statisticians is crucial for bridging the vocabulary gap
  • Data visualization can help tell compelling stories with data
  • Ethical considerations are important when interpreting statistical results

  Introduction: Embracing the Applied Statistician Within

  We often think statistics is only for experts, but it’s key in many areas. Embracing our applied statistician lets us use data well and make smart choices³.

  The Importance of Statistics in Research

  Statistics is vital in research. It helps design studies, analyze data, and understand results. It’s essential for market research, testing new treatments, or studying relationships³.

  Overcoming the Divide Between “Real” Statisticians and Non-Statisticians

  Many see a big gap between “real” statisticians and others. But we can bridge it by being our own applied statistician. This means learning, being curious, and keeping up with new stats³⁴.

  Statistics is more than just numbers. It helps us understand the world. By learning and applying stats, we become better at solving problems and making decisions³⁴.

  Embracing our applied statistician is challenging but rewarding. Working with statisticians helps us find new insights and make a difference³⁴.

  The Long Game: Building Statistical Expertise Over Time

  Becoming an effective applied statistician takes time, patience, and a commitment to learning. It’s not about quick fixes. Progress in this field is slow, over months and years, not hours and days.

  We must trust the process and fill our knowledge gaps bit by bit. Building statistical expertise is a marathon, not a sprint. With long-term learning and dedication to statistics skill development, we can get better over time.

  “Becoming an effective applied statistician is not something that can be learned quickly. It’s a long-term process that requires patience and persistence.”⁵

  Success comes from seeing we’re all on a journey together. Acknowledging challenges and celebrating small wins helps build a supportive community. We’re all learning to make sense of data and drive change.

  The path to statistical expertise is a steady climb, not a race. With the right mindset and effort, we can all get better at using statistics. This opens up new chances for making data-driven decisions and solving problems. Explore the power of advanced data visualization tools to help your journey.

  The journey to becoming skilled in applied statistics is long but rewarding. By playing the long game and building expertise over time, we can change how we see data. This leads to new insights that drive innovation and progress. Let’s start this journey together, step by step, and make the most of our long-term learning experience.

  Numeracy: Getting Comfortable with Numbers

  Exploring applied statistics shows us that being good with numbers is key, even if you’re not a math whiz. Numeracy skills help us understand and work with numbers. They’re crucial for making sense of data and analysis.

  Resources for Improving Numeracy Skills

  There are many ways to get better at numeracy skills if you’re not a pro at math. You can find resources for basic math, probability, and even complex topics like logarithms and matrix algebra. There’s something for everyone.

  • Online courses on platforms like Coursera, edX, and Udemy offer fun ways to improve your numerical literacy.
  • Apps and games make learning math skills fun and easy, letting you practice anywhere.
  • Books and textbooks are great for in-depth learning and can be your go-to guides for numeracy competencies.

  Improving your comfort with numbers is an ongoing process. With the right tools and effort, you can boost your mathematical proficiency. This will make you more confident and skilled in applied statistics⁶⁷.

  Resource	Description	Recommendation Level
  Khan Academy	Free online platform with a vast library of educational videos and exercises covering a wide range of mathematical topics, from basic arithmetic to advanced calculus.	High
  Mathisfun.com	Interactive website that offers games, puzzles, and lessons to make learning math fun and engaging for all ages.	Medium
  The Humongous Book of Basic Math and Pre-Algebra Problems	Comprehensive textbook filled with step-by-step explanations and practice problems to strengthen fundamental mathematical skills.	High

  

  “Numeracy is not just about numbers, but about making common sense of the world. It’s about asking questions, making informed decisions, and understanding the consequences of those decisions.” – John Allen Paulos, American mathematician and writer

  Using these resources for improving math skills, we can all get better at numeracy abilities. This helps us do more effective data analysis in our applied statistics work⁶⁷.

  The Philosophical Foundations of Statistics

  Many people see statistics as just about numbers. But, it’s really about deep questions that shape the field. Exploring the philosophy of statistics shows us how we know what we think we know. It helps us understand evidence, inference, and how statistical and scientific hypotheses relate.

  Understanding the “Why” Behind Statistical Methods

  Statistics isn’t just about numbers; it helps us make sense of our world⁸. It uses models to estimate unknown things from data⁸. These models aim to be unbiased and consistent for reliable results⁸.

  Nonparametric statistics looks at models without strict assumptions about data types⁸. It uses methods like the empirical measure to estimate true distributions⁸. As sample sizes grow, these methods get closer to the truth⁸. The law of large numbers is key, showing how sample estimators approach true values over time⁸.

  ⁹Statistics is about testing hypotheses with data, organizing it into structured sets⁹. The philosophy of statistics helps us understand statistical methods and their results⁹. It covers topics like induction and the meaning of probabilities in statistics⁹.

  ⁹Classical statistics deals with testing hypotheses and understanding evidence⁹. Bayesian statistics looks at inference and prior probabilities⁹. It also talks about comparing models and data reduction⁹. The philosophy of statistics touches on many areas, including evidence and causality⁹.

  ¹⁰Sir Ronald Fisher introduced a method to simplify data into key numbers¹⁰. His approach assumed data is a random sample from a larger population¹⁰. However, this can lead to bias if not done carefully¹⁰.

  ¹⁰Logical Positivism says knowledge comes from what we see and certainty is key¹⁰. Realist Philosophy believes knowledge is about the hidden truths and accepts uncertainty¹⁰. Statisticians struggle to define probability, with different views on the subject¹⁰.

  ¹⁰Real statistics aim to uncover the real world’s structures, not just what we see¹⁰. Defining causality is hard, with ongoing debates¹⁰. This shows the mystery and complexity of the field¹⁰.

  “Statistics is not just about crunching numbers; it’s about making sense of the world around us.”

  Exploring the philosophy of statistics helps us understand why we use certain methods. It makes us better at using data in real situations. This knowledge is key for anyone looking to make sense of statistics.

  The Versatility of Linear Models

  As non-statisticians, we often feel overwhelmed by the many statistical tests we need to learn¹¹. But, there’s a powerful tool that can make our analysis easier: linear models. These models are versatile and can be used in many ways to help us understand our data better¹¹.

  Mastering a Single Powerful Tool

  Instead of trying to learn many tests, we can focus on linear models¹¹. These models help us see how variables are related, make predictions, and get insights from our data¹¹. Learning one powerful tool helps us build a strong base in statistics and use it in many areas¹¹.

  Linear models are key in fields like data science, healthcare, finance, marketing, and social sciences¹¹. They let us do things like regression analysis, hypothesis testing, and predictive modeling¹¹. Whether we’re studying customer behavior or a new medical treatment, linear models give us valuable insights¹¹.

  To use linear models well, we need to understand the stats behind them¹¹. Knowing about linear regression, ANOVA, and other techniques lets us use these tools with confidence¹¹. This way, we can go beyond just a few tests and use a single powerful tool for many tasks¹¹.

  Mastering linear models changes the game in statistical analysis¹¹. By focusing on this tool, we can see new insights and make smart decisions with data¹¹. In fields like healthcare, marketing, or others, linear models help us handle complex data and make a real impact¹¹.

  “Linear regression is an important statistical method for analyzing medical data¹². Regression analysis helps find and understand relationships among many factors¹². But, there are pitfalls to watch out for when using linear regression¹².”

  By using linear models, we bridge the gap between “real” statisticians and non-statisticians¹¹. With practice and a deeper understanding, we can fully use statistical analysis and solve problems in new ways¹¹.

  Grasp the Basics of UnderstandingStatistical

  Statistics for Non-Statisticians: Making Sense of Your Data in 2024

  In 2024, learning data analysis is key for those without a strong stats background. This workshop is here to help us, non-statisticians, understand our data better. It aims to use data to make smart choices.

  Professor Sun leads this workshop, aiming to connect “real” statisticians with non-statisticians¹³. He’s a Google data scientist and will soon lead the Program in Data Science at Stanford¹³. He’s made courses more accessible and wrote a free book to help students start with data early¹³.

  Now, making data-driven choices is key. Non-statisticians need to grasp stats and apply them¹⁴. With lots of research being wasted, it’s vital for us to understand our data well¹⁴.

  This workshop will cover data types, summary stats, testing hypotheses, and meta-analysis¹³. Professor Sun’s teaching will boost our confidence in data analysis. This will help us succeed in 2024 and beyond.

  If you’re in clinical research, regulatory affairs, or medical writing, this workshop is for you. It will give you the skills and mindset to understand your data and find important insights. Don’t miss this chance to improve your data skills and lead in 2024.

  Communicating with Statisticians as a Non-Statistician

  As non-statisticians, we often work with statisticians to understand data and make decisions. But, our different vocabularies and views on stats can make talking hard. Learning to bridge this gap is key to working well together and getting the most from data.

  Bridging the Vocabulary Gap

  One big challenge is the special terms statisticians use. To get past this, we should learn the basics of stats and their terms¹⁵. Starting to work with a statistician early, even months before a project, helps us all understand each other better¹⁵. This way, we can make sure the stats fit into our plans well.

  It also helps to explain things simply and use examples everyone can get². Pictures like charts and diagrams make stats clearer². By making our messages fit what others need and like, we make sure they get the stats in a way that grabs their attention.

  To really talk well with statisticians, be proactive, curious, and open-minded¹⁶. By keeping language simple, avoiding hard terms, and using pictures, we make stats easier for others to grasp¹⁶. This teamwork approach not only helps us understand better but also makes our work together more powerful and informed.

  “Effective data visualization is highlighted as a crucial method to help non-statisticians understand data insights, patterns, and trends.”¹⁶

  Strategies for Communicating with Statisticians	Benefits
  Involve statisticians early in the research process Develop a shared language and understanding of statistical concepts Simplify explanations and use relatable examples Utilize visuals like charts, graphs, and diagrams Tailor communication to the specific audience Avoid jargon and use plain language	Enhance collaboration and avoid misunderstandings Improve understanding of statistical concepts Facilitate more meaningful and productive partnerships Lead to more informed and data-driven decision-making

  By using these tips, we can talk better with statisticians, close the vocabulary gap, and work together smoothly to get the best from data¹⁵²¹⁶.

  Data Visualization: Telling Stories with Data

  We, as non-statisticians, need to use data visualization to share our findings clearly. It’s not just about making charts look nice. It’s a key way to turn complex info into stories that grab people’s attention¹⁷.

  When dealing with data, it’s important to understand its distribution, spot patterns, and see how things interact¹⁷. Tools like ggplot or Tableau let us see our data in a visual way, using simple stats and graphs¹⁷. We should aim for simple, clear methods to find important insights¹⁷.

  Data visualization is now widely used, thanks to people like Hans Rosling. His TED Talks, seen over 50 million times, show how colorful, interactive graphics can change minds and spark action¹⁸. By using visuals, we can make data stories that catch and keep our audience’s interest¹⁹.

  To tell data stories well, we need creativity, good communication skills, and the ability to make a story that connects with people¹⁹. Whether it’s sports, health, or sales data, visualization can turn numbers into clear, useful insights¹⁹.

  Key Elements of Effective Data Storytelling	Description
  Understanding the Audience	Tailor the narrative to the specific needs and interests of the audience.
  Hooking with a Compelling Conflict	Start with a captivating problem or challenge that grabs the audience’s attention.
  Focusing on a Key Message	Identify the central insight or call to action, and build the story around it.
  Presenting Relevant Data	Select the most pertinent data points to support the narrative and key message.
  Supporting the Core Message	Weave the data and visualizations throughout the story to reinforce the central theme.

  As we get better at data visualization, we can make our data into stories that inspire action and bring about real change¹⁹.

  “Data storytelling is often viewed as the last mile in data analytics, engaging audiences effectively to transform them from passive listeners to active participants.”¹⁹

  Interpreting Statistical Results

  Understanding statistical results can be tough for non-statisticians. It’s key to be careful and nuanced when looking at these findings. Statistics are crucial in data²⁰. Knowing how to interpret them correctly helps in making better decisions.

  Avoiding Statistical Pitfalls

  Many people get confused about p-values. A p-value ≤ 0.05 often means a result is statistically significant²⁰. But, over 63% of researchers don’t get p-values right²¹. Remember, a low p-value doesn’t always mean there’s a big effect. Other stats should be looked at too.

  It’s also a mistake to say there are no effects if the p-value is ≥ 0.05²¹. This can miss important insights. Researchers should think about the study’s power and limits before jumping to conclusions.

  Sample size and effect size matter a lot. They affect power and p-values²¹. Researchers need to keep these in mind to avoid wrong interpretations.

  Overcoming Common Statistical Misconceptions

  Some think a 99% significance level is always better than 95%²⁰. But, a higher level can miss important findings too. It depends on the situation.

  Another mistake is relying only on the p-value. It’s just a starting point. Effect size and power are also key for a full understanding²¹.

  By understanding and avoiding these mistakes, non-statisticians can better interpret statistical results. This leads to smarter decisions and more accurate conclusions.

  

  Ethical Considerations in Data Analysis

  When we dive into data analysis, we must think about the ethical sides of our work. Ethical data analysis is more than just numbers. It’s about respecting privacy, avoiding bias, being open, and using algorithms wisely²².

  In our data-driven world, we need to follow the best responsible data practices. This means keeping people’s privacy safe, making sure data is correct, and stopping biases that could be unfair²³.

  As non-statisticians, we can bring a new view to data ethics. By knowing the deep reasons behind statistics and how to share our findings clearly, we can make our work more meaningful²².

  Ethical Principle	Practical Application
  Privacy and Confidentiality	Ensure the secure storage and responsible handling of sensitive data.
  Transparency and Accountability	Disclose data sources, methodologies, and potential biases to stakeholders.
  Environmental Impact	Consider the energy consumption and carbon footprint of data processing and storage.

  By following these ethical rules, we can use data analysis for good and build trust in our work. As we move forward in making decisions with data, let’s always aim for the highest ethical data analysis and responsible data practices²³.

  “In a world awash with data, the ethical use of that data is paramount. As non-statisticians, we have a responsibility to wield that power responsibly and with a steadfast commitment to the greater good.”

  Conclusion

  As we end our journey, it’s clear that becoming an applied statistician within is a smart move for non-statisticians in 2024 and later. We’ve learned a lot about how to understand the data around us²⁴.

  Learning about statistics helps us question data sources, know about errors, and check if statistics are correct²⁴. This way, we can make better choices and see things more clearly in today’s complex world²⁴. It makes us better at spotting biases and making sense of data²⁴.

  The main points are clear: stay curious, improve your math skills, and be careful with data²⁵. With effort and a desire to learn, we can use the tips from this article to handle data and statistics with confidence²⁵. This way, we become more informed and critical thinkers, ready for the future’s challenges and chances²⁴²⁵.

  FAQ

  What is the importance of statistics in research?

  Statistics is key in many sciences. It helps researchers analyze data and make smart choices. This article aims to help those not familiar with statistics improve their skills.

  How can non-statisticians overcome the divide between “real” statisticians and themselves?

  The article talks about the gap between “real” statisticians and others. An “applied statistician” shares ways for non-statisticians to get better at stats and feel more confident.

  Why is building statistical expertise a long-term process?

  Learning to be an applied statistician takes time. It’s not something you can pick up quickly from blogs. The article says to be patient and keep working, as progress takes months and years.

  What are the key numerical skills required for effective applied statistics?

  You need skills like doing simple math, understanding probabilities, and knowing about logarithms and matrix algebra. The article lists resources to help improve these skills.

  Why is it important for non-statisticians to engage with the philosophical foundations of statistics?

  It’s important for non-statisticians to learn about the history and ideas behind statistics. This includes thinking about how we know things, the nature of evidence, and how statistical and scientific hypotheses relate.

  How can the linear model replace the need to learn numerous statistical tests and procedures?

  The linear model is a powerful tool for many statistical tasks. It’s important for non-statisticians to learn it well, as it can help with a wide range of analyses.

  What are the communication challenges between non-statisticians and statisticians, and how can they be addressed?

  There are often communication issues due to different vocabularies and understanding. The article offers tips for non-statisticians to talk better with statisticians and understand each other’s terms and ideas.

  Why is data visualization crucial for non-statisticians in making sense of their data?

  Data visualization is key for non-statisticians to understand their data. It helps tell stories with data and share insights clearly with others.

  What are the common pitfalls and misconceptions in interpreting statistical results, and how can non-statisticians avoid them?

  There are pitfalls that can lead to wrong interpretations of stats. The article gives advice on how to be careful and make accurate conclusions from stats.

  Why is it important for non-statisticians to consider the ethical implications of their data practices?

  Non-statisticians should think about the ethics of their data work. This includes issues like privacy, bias, and using stats responsibly. They should analyze data with a strong ethical focus.

  Source Links

  1. https://magazine.amstat.org/blog/2024/06/03/impactstories/
  2. https://www.linkedin.com/advice/0/how-can-you-help-non-statisticians-understand-statistical-jq7ye
  3. https://errorstatistics.com/2024/07/11/guest-post-ron-kenett-whats-happening-in-statistical-practice-since-the-abandon-statistical-significance-call-5-years-ago/
  4. https://theeffectivestatistician.com/stepping-outside-of-our-functions-as-statisticians-with-your-career/
  5. https://sloanreview.mit.edu/article/ai-and-statistics-perfect-together/
  6. https://www.linkedin.com/pulse/making-data-work-accessible-barton-poulson-phd-aqh4c
  7. https://medium.com/@sahin.samia/a-roadmap-to-self-study-statistics-for-data-science-in-2024-c98f8e2dc49c
  8. https://mathoverflow.net/questions/450821/recommendation-for-learning-mathematical-statistics-and-probability
  9. https://plato.stanford.edu/archIves/spr2024/entries/statistics/
  10. https://www.linkedin.com/pulse/four-flaws-foundations-statistics-asad-zaman-1f
  11. https://www.scaler.com/blog/statistics-for-data-science/
  12. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2992018/
  13. https://humsci.stanford.edu/feature/versatility-statistics-and-data-science-across-majors
  14. https://brokenscience.org/mindless-statistics/
  15. https://globalhealth.duke.edu/news/10-things-know-about-working-statistician
  16. https://www.linkedin.com/advice/3/you-need-work-non-statisticians-whats-best-way-get-them-yxzgf
  17. https://jlmc.medium.com/so-you-need-to-analyze-some-data-4a0f865b3c14
  18. https://www.linkedin.com/pulse/telling-story-data-hans-rosling-sal-huante
  19. https://www.discoverdatascience.org/articles/data-storytelling/
  20. https://medium.com/@sutirnac1998/how-to-interpret-p-value-as-a-non-statistician-06ddb3061c0f
  21. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9437930/
  22. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4465206/
  23. https://www.amstat.org/your-career/ethical-guidelines-for-statistical-practice
  24. https://www.linkedin.com/pulse/statistics-can-save-world-wayne-lee
  25. https://chriszhu12.medium.com/questions-that-statistics-cant-answer-84a0ecdc1921