Category: statistics
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Stop the Count! Why Putting A Time Limit on Metrics is Critical for Fast and Accurate Experiments
Stop the Count! Why Putting A Time Limit on Metrics is Critical for Fast and Accurate Experiments Why your experiments might never reach significance Photo by Andrik Langfield on Unsplash Introduction Experiments usually compare the frequency of an event (or some other sum metric) after either exposure (treatment) or non-exposure (control) to some intervention. For example:…
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Lessons from COVID-19: Why Probability Distributions Matter
Lessons from COVID-19: Why Probability Distributions Matter Understanding Distributions with Extremes: Probability for Data Science Series (END) Continue reading on Towards Data Science » Sunghyun Ahn Go to original source
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How to Ensure the Stability of a Model Using Jackknife Estimation
How to Ensure the Stability of a Model Using Jackknife Estimation How to ensure the robustness of a model and detect influential data observations Continue reading on Towards Data Science » Paula LC Go to original source
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Measuring Cross-Product Adoption Using dbt_set_similarity
Measuring Cross-Product Adoption Using dbt_set_similarity Enhancing cross-product insights within dbt workflows Introduction For multi-product companies, one critical metric is often what is called “cross-product adoption”. (i.e. understanding how users engage with multiple offerings in a given product portfolio) One measure suggested to calculate cross-product or cross-feature usage in the popular book Hacking Growth [1] is…
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Jingle Bells and Statistical Tests
Jingle Bells and Statistical Tests Data Types, Hypotheses and Statistical Tests That Fit Them with Festive Christmas Market Examples🎄🎅🎡 Continue reading on Towards Data Science » Gizem Kaya Go to original source
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Probability Distributions: Poisson vs. Binomial Distribution
Probability Distributions: Poisson vs. Binomial Distribution Using Soccer to Understand the Difference Between Poisson & Binomial: Probability for Data Science Series (3) Continue reading on Towards Data Science » Sunghyun Ahn Go to original source
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Adapted Prediction Intervals by Means of Conformal Predictions and a Custom Non-Conformity Score
Adapted Prediction Intervals by Means of Conformal Predictions and a Custom Non-Conformity Score How confident should I be in a machine learning model’s prediction for a new data point? Could I get a range of likely values? Image by author When working on a supervised task, machine learning models can be used to predict the outcome for…
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When Averages Lie: Moving Beyond Single-Point Predictions
When Averages Lie: Moving Beyond Single-Point Predictions The Case for Predicting Full Probability Distributions in Decision-Making Some people like hot coffee, some people like iced coffee, but no one likes lukewarm coffee. Yet, a simple model trained on coffee temperatures might predict that the next coffee served should be… lukewarm. This illustrates a fundamental problem…
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How to Apply the Central Limit Theorem to Constrained Data
How to Apply the Central Limit Theorem to Constrained Data What can we say about the mean of data distributed in an interval [a, b]? Continue reading on Towards Data Science » Ryan Burn Go to original source
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Why “Statistical Significance” Is Pointless
Why “Statistical Significance” Is Pointless Here’s a better framework for data-driven decision-making Continue reading on Towards Data Science » Samuele Mazzanti Go to original source
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Water Cooler Small Talk: Simpson’s Paradox
Water Cooler Small Talk: Simpson’s Paradox Is your data tricking you? What can you do about it? Continue reading on Towards Data Science » Maria Mouschoutzi, PhD Go to original source