The hottest Statistics Substack posts right now

And their main takeaways
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Top Technology Topics

Data Science Weekly - Issue 122

Data Science Weekly Newsletter • 19 implied HN points • 24 Mar 16
🕹 Technology Statistics
  1. P-values are often seen as the gold standard for determining if a study is good, but experts warn they can be misleading and overused.
  2. Machine learning is changing how businesses operate and is being used in various real-world applications, showing its growing importance.
  3. Deep learning techniques are making big strides in generative models, helping to produce and understand complex data like images and text.

Data Science Weekly - Issue 121

Data Science Weekly Newsletter • 19 implied HN points • 17 Mar 16
🕹 Technology Statistics
  1. A new AI with 30 years of knowledge is finally ready to be used in the real world. This shows how far AI has come in understanding and processing information.
  2. There's a new effort to monitor police behavior using algorithms to predict misconduct. This technology aims to improve police interactions with the public.
  3. Using pie charts can be misleading; better alternatives exist for visualizing data. There are effective ways to present statistics that make information clearer.

Data Science Weekly - Issue 110

Data Science Weekly Newsletter • 19 implied HN points • 31 Dec 15
🕹 Technology Statistics
  1. Some websites offer tools and training to help you create quick data visualizations, which can be really useful if you're learning to use D3.js.
  2. It's important to highlight your personal projects on your data science resume, as they can showcase your skills and practical experience.
  3. There are many interesting articles and studies out there about data's role in health, global warming, and machine learning that can deepen your understanding of these topics.

Data Science Weekly - Issue 107

Data Science Weekly Newsletter • 19 implied HN points • 10 Dec 15
🕹 Technology Statistics
  1. An algorithm can identify influential universities based on Wikipedia data, revealing some unexpected rankings.
  2. Many commonly accepted rules in statistics might not hold true, and it's crucial to question them.
  3. Machine learning can lead to significant maintenance costs despite offering quick results, known as technical debt.

Data Science Weekly - Issue 98

Data Science Weekly Newsletter • 19 implied HN points • 08 Oct 15
🕹 Technology Statistics
  1. Data science can help social good organizations, but they need more than just good intentions to make a real impact. Following certain principles can help these efforts succeed.
  2. The random walk hypothesis is a way to explore market behaviors and randomness. Understanding it can help in analyzing financial markets more effectively.
  3. Teaching statistics can be challenging, and it's important to make it easier for students. If students find it complicated, educators should look at their teaching methods.
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Data Science Weekly - Issue 95

Data Science Weekly Newsletter • 19 implied HN points • 17 Sep 15
🕹 Technology Statistics
  1. Artificial intelligence is growing and changing rapidly, with experts like Eric Schmidt discussing its future impacts.
  2. There are innovative uses of machine learning, like generating music and analyzing large datasets, showing its versatility across different fields.
  3. Resources for learning, such as cheat sheets and books on machine learning, can help anyone interested in diving deeper into data science.

Data Science Weekly - Issue 94

Data Science Weekly Newsletter • 19 implied HN points • 10 Sep 15
🕹 Technology Statistics
  1. Data science combines skills from statistics and computer science to analyze and interpret complex data. It's a growing field that's seen as crucial for modern businesses.
  2. Neural networks are important in deep learning, allowing computers to identify patterns and make predictions. They can be complex but are essential for many applications like image and speech recognition.
  3. Understanding foundational topics, like probability and linear algebra, is key for anyone wanting to succeed in data science. There are plenty of resources available to help learn these subjects.

Data Science Weekly - Issue 92

Data Science Weekly Newsletter • 19 implied HN points • 27 Aug 15
🕹 Technology Statistics
  1. Google is developing new algorithms, called 'Thought Vectors,' that could allow computers to understand logic and have natural conversations.
  2. There's an article showing how data can prove which songs from the 90s remain timeless by comparing their Spotify plays over the years.
  3. Machine learning and statistics aim to solve similar problems but use different methods, highlighting the important distinctions between the two fields.

Data Science Weekly - Issue 91

Data Science Weekly Newsletter • 19 implied HN points • 20 Aug 15
🕹 Technology Statistics
  1. Artificial Intelligence is growing fast, with 855 companies and $8.75 billion in funding. It plays a big role in different markets today.
  2. Principal Component Analysis can help analyze images, like fashion designs, by breaking them down into key features. This technique is useful in various fields.
  3. Data science can assist in city planning by using data to revitalize struggling neighborhoods. This approach helps cities manage resources better.

Data Science Weekly - Issue 90

Data Science Weekly Newsletter • 19 implied HN points • 13 Aug 15
🕹 Technology Statistics
  1. Sorting algorithms can be visualized in a fun way through animations, making it easier to understand how they work.
  2. AI tools, like Baidu's medical robot, can help provide quick health advice based on symptoms, improving access to healthcare.
  3. Machine learning techniques are being used in diverse fields, from predicting wine prices to improving speech recognition systems.

Machine learning is not just statistics

The Palindrome • 2 implied HN points • 09 Aug 23
🔬 Science Statistics
  1. Machine learning heavily utilizes statistics, but it is not just applied statistics.
  2. Probability enables reasoning about uncertainty, while statistics quantifies and explains it.
  3. Probability theory provides tools to deal with missing information and formulate models with likelihood measures.

Data Science Weekly - Issue 85

Data Science Weekly Newsletter • 19 implied HN points • 09 Jul 15
🕹 Technology Statistics
  1. PhD candidates often struggle to apply for data science jobs, but understanding industry expectations can help them succeed.
  2. AI tools are evolving quickly, with projects teaching machines to analyze and classify complex data, like galaxy images and social media content.
  3. There's a growing need for data scientists to address big issues, like obesity, by using available health data to create innovative solutions.

Data Science Weekly - Issue 77

Data Science Weekly Newsletter • 19 implied HN points • 14 May 15
🕹 Technology Statistics
  1. Data scientists often come from different backgrounds, not just math or computer science. Learning some software development skills can be very helpful for data scientists.
  2. Machine learning has advanced to a point where algorithms can outperform experts in certain fields, like art history. This shows how powerful technology can be in analyzing complex data.
  3. Understanding statistical methods, like p-values, is important for good science. It's crucial to scrutinize every step of data analysis, not just the final results.

Data Science Weekly - Issue 75

Data Science Weekly Newsletter • 19 implied HN points • 30 Apr 15
🕹 Technology Statistics
  1. A new algorithm can speed up 3-D protein structure discovery by a lot, making research faster and more efficient.
  2. Bob Ross's artwork used a consistent style that can be analyzed statistically, showing how data can help us understand artistic patterns.
  3. Automation is becoming important in data science, helping to choose and evaluate machine learning models more easily.

Data Science Weekly - Issue 67

Data Science Weekly Newsletter • 19 implied HN points • 05 Mar 15
🕹 Technology Statistics
  1. Flickr uses deep learning to automatically label images, which helps with the huge number of daily uploads. This shows how technology can improve organization and accessibility of visual data.
  2. Data visualization is becoming essential in journalism, as it helps tell stories more effectively than traditional text and images. This shift is changing the way information is communicated to the public.
  3. Machine learning is being applied in drug discovery, showing its potential to find effective treatments for various diseases. This highlights how data science can make a significant impact on health and medicine.

Data Science Weekly - Issue 64

Data Science Weekly Newsletter • 19 implied HN points • 12 Feb 15
🕹 Technology Statistics
  1. There are algorithms that can recognize beauty in portraits, showing how technology can analyze aesthetic qualities. This could change how we view photography and art.
  2. Machine learning isn't just for tech; it can help in fields like journalism and social work, making tasks easier and spreading important information.
  3. You don't need heavy math skills to be a data analyst. There are many roles where you can contribute without being a math expert.

Data Science Weekly - Issue 59

Data Science Weekly Newsletter • 19 implied HN points • 08 Jan 15
🕹 Technology Statistics
  1. Nvidia is showcasing cool technology that lets computers recognize objects in real-time using deep learning.
  2. There's a new field emerging that focuses on how humans interact with data, emphasizing the need for better ethics in data use.
  3. Creating a strong data science portfolio is important, and there are many project ideas and techniques you can use to get started.

The hot hand fallacy

Optimally Irrational • 2 HN points • 06 Jun 23
🔬 Science Statistics
  1. The hot hand fallacy is a famous cognitive bias related to probability judgments.
  2. People often underestimate the presence of streaks in random sequences, leading to the hot hand fallacy.
  3. Game theory suggests that momentum in sports, like the hot hand, may have strategic reasons to exist.

Data Science Weekly - Issue 53

Data Science Weekly Newsletter • 19 implied HN points • 27 Nov 14
🕹 Technology Statistics
  1. Teaching creativity through programming can be fun, as shown by a class project where students made Twitter bots.
  2. Research from Yahoo Labs helps us understand creativity in short videos like Vine, revealing new ways to analyze creative content.
  3. Using social media data can provide insights into complex topics, like unemployment trends, in a more cost-effective way than traditional methods.

Data Science Weekly - Issue 45

Data Science Weekly Newsletter • 19 implied HN points • 02 Oct 14
🕹 Technology Statistics
  1. Data science is important for creating content that goes viral, as seen with BuzzFeed's strategies. Understanding what people like can help predict online trends.
  2. Machine learning can be used in real-world applications like gender detection on social media. This shows how technology can analyze and understand large amounts of user data.
  3. Making math education relevant is crucial. Teaching statistics first could help students understand data better and see its importance in everyday life.

Data Science Weekly - Issue 43

Data Science Weekly Newsletter • 19 implied HN points • 18 Sep 14
🕹 Technology Statistics
  1. Data science is creating new opportunities and products, showing its importance in today's tech world.
  2. Understanding causality in data can help businesses grow and make better decisions.
  3. There's a need for updating educational materials in statistics to keep up with advancements in data science.

Data Science Weekly - Issue 42

Data Science Weekly Newsletter • 19 implied HN points • 11 Sep 14
🕹 Technology Statistics
  1. Data science and machine learning are rapidly evolving fields, and staying updated is crucial for practitioners. Learning what works and what pitfalls to avoid is important for success.
  2. Graphs are valuable tools for organizing and relating information in data analysis. Techniques like document classification demonstrate how effective graph-based methods can be.
  3. Understanding the relationship between statistics and data science can identify both challenges and opportunities. It's important for statistics to adapt and remain relevant in the data science landscape.

Data Science Weekly - Issue 37

Data Science Weekly Newsletter • 19 implied HN points • 07 Aug 14
🕹 Technology Statistics
  1. Deep learning can enhance music recommendations, like the approach used by Spotify to suggest songs based on content.
  2. Algorithms can be very accurate in predicting outcomes, such as Supreme Court rulings, by analyzing historical data.
  3. New technology can even extract audio from video by examining tiny vibrations, showcasing how advanced data analysis can be.

Data Science Weekly - Issue 35

Data Science Weekly Newsletter • 19 implied HN points • 24 Jul 14
🕹 Technology Statistics
  1. Dropout is a technique used to prevent neural networks from overfitting, making them more effective. It helps improve the models without making them too slow to use.
  2. The tidyr package helps to organize data so it's easier to work with, visualize, and analyze in R. Tidying data simplifies the tasks of data cleaning and exploration.
  3. Airbnb is using customer reviews and host descriptions to create smarter travel recommendations. They are leveraging big data to enhance the travel experience for customers.

Data Science Weekly - Issue 34

Data Science Weekly Newsletter • 19 implied HN points • 17 Jul 14
🕹 Technology Statistics
  1. A new computer program can find rare genetic disorders just by looking at photos of families. This shows how technology can help identify health issues more easily.
  2. Probabilistic programming is a growing area of research that could improve machine intelligence. It's complex but important for understanding how to make predictions.
  3. Data for Good is a new site where data scientists can showcase projects that make a positive impact on the world. It's exciting to see tech being used for social good.

Tuesday Variety Pack

Donkeyspace • 2 implied HN points • 18 Apr 23
🕹 Technology Statistics
  1. David Deutsch explains why he's not worried about AGI.
  2. Peli Grietzer explores the intersection of poetry, art, philosophy, and AI.
  3. Gregory Chaitin's lecture delves into the foundational questions of mathematics and computers.

Data Science Weekly - Issue 29

Data Science Weekly Newsletter • 19 implied HN points • 12 Jun 14
🕹 Technology Statistics
  1. Data science is a popular and exciting field, with many people wanting to learn how to become a data scientist.
  2. Using analytical techniques, like regression discontinuity, can help understand complex issues, such as the impact of services like Uber on DUI rates.
  3. Specialized tools and libraries can offer better statistical analysis capabilities than standard math libraries, making them more appealing for statisticians.

Data Science Weekly - Issue 26

Data Science Weekly Newsletter • 19 implied HN points • 22 May 14
🕹 Technology Statistics
  1. Data science is critical for growth, as seen in Twitch's success story. Understanding data can really help companies improve their services and reach more users.
  2. Neural networks are a fascinating topic in data science that is gaining a lot of attention nowadays. They are particularly useful for deep learning and building advanced machine learning models.
  3. Big data hype might fade, but the importance of statistics will remain. It’s essential to understand data correctly to avoid misleading conclusions and improve decision-making.

Data Science Weekly - Issue 20

Data Science Weekly Newsletter • 19 implied HN points • 10 Apr 14
🕹 Technology Statistics
  1. Understanding neural networks can be easier with low-dimensional models, where we can use visualizations to see how they behave and learn.
  2. Building a data-driven organization involves encouraging team members to make decisions based on data rather than gut feelings.
  3. Machine Learning has its challenges, for example in self-driving car research, there are many expectations that might not be fulfilled as quickly as we hope.

Confirmation Bias: A Bayesian Interpretation

In My Tribe • 1 HN point • 28 Feb 24
🚌 Education Statistics
  1. Having a strong prior belief is fine, but bias comes in when one refuses to consider evidence against that belief.
  2. Using Bayesian reasoning means weighing new evidence against what you believed before, termed your 'prior.'
  3. Bias occurs when someone puts a negative weight on new information, ignoring evidence that contradicts their prior beliefs.

Data Science Weekly - Issue 10

Data Science Weekly Newsletter • 19 implied HN points • 30 Jan 14
🕹 Technology Statistics
  1. Data mining can help predict which countries will win medals in the Winter Olympics. It can reveal trends and reasons behind particular nations' success.
  2. Deep learning aims to make computers think like humans. It showcases the progress in teaching machines to learn and improves how they process information.
  3. Data science plays a crucial role in various industries, like Foursquare and New York's Fire Department, to analyze data and improve services or predict events.

Churn Paradox

Magis • 1 HN point • 20 Feb 24
💼 Business Statistics
  1. Simpson's paradox teaches us that aggregate metrics can lead to wrong conclusions when not considering the composition of the aggregate.
  2. A common construction of churn metrics can be misleading; decreasing aggregate churn rate may not always mean the revenue base quality is improving, and sudden increases can be due to new customers rather than a decline in quality.
  3. Churn paradox occurs when fixed-period aggregate churn rates ignore the sizes of customer acquisition cohorts, leading to skewed conclusions about customer retention and revenue base.

Statistics versus Understanding: The Essence of What Ails Generative AI

Marcus on AI • 1 HN point • 13 Feb 24
🕹 Technology Statistics
  1. Generative AI often relies on statistics as a shortcut for true understanding, leading to shaky foundations and errors.
  2. Challenges arise when generative AI systems fail to grasp complex concepts or contexts beyond statistical relationships.
  3. Examples in various domains show the struggle between statistical frequency and genuine comprehension in generative AI.

Bayesian A/B Testing

Boris Again • 1 HN point • 18 Apr 23
🔬 Science Statistics
  1. A/B testing compares two treatments to measure impact.
  2. Frequentist A/B testing involves hypothesis formulation, experiment design, and statistical testing.
  3. Bayesian A/B testing incorporates prior beliefs to estimate probabilities directly.

Entropic emanations

Simplicity is SOTA • 1 HN point • 13 Mar 23
🔬 Science Statistics
  1. Log loss is a proper scoring function that incentivizes honest prediction and has intrinsic meaning.
  2. Cross entropy in multiclass problems is based on log loss, which compares predictions to outcomes on a log scale.
  3. Modifying cross entropy to consider negative classes in loss functions may impact gradient calculation simplicity and model fitting.

Differential privacy, an easy case

Accuracy and Privacy • 1 HN point • 02 Jan 19
🕹 Technology Statistics
  1. Differential privacy is a mathematical definition of privacy specifically designed for protecting personal data in a world of big data and computation.
  2. Privacy protection in differential privacy comes from adding randomness or noise to data before publishing, where more noise equals greater privacy protection.
  3. There is a tradeoff between accuracy and privacy in differential privacy, as the level of uncertainty introduced for privacy protection can impact the accuracy of conclusions drawn from the data.

Study 1b: This One Weird Trick does NOT cause incorrectness cascades

From AI to ZI • 0 implied HN points • 20 Apr 23
🔬 Science Statistics
  1. Study found that changing question format from multiple choice to true/false did not significantly affect GPT-3.5's tendency to prefer factual answers
  2. The study showed mixed results for the hypotheses tested regarding the accuracy of answers based on question format and context
  3. Despite some limitations and deviations from the original plan, the study provided insights on how GPT-3.5 performs in providing factual answers