Strong statistical understanding is crucial for data scientists to interpret results accurately, avoid misleading conclusions, and make informed decisions. It's a foundational skill that complements technical programming abilities.
* **Statistical vs. Practical Significance:** Don't automatically act on statistically significant results. Consider if the effect size is meaningful in a real-world context and impacts business goals.
* **Sampling Bias:** Be aware that your dataset is rarely a perfect representation of the population. Identify potential biases in data collection that could skew results.
* **Confidence Intervals:** Report ranges (confidence intervals) alongside point estimates to communicate the uncertainty of your data. Larger intervals indicate a need for more data.
* **Interpreting P-Values:** A p-value indicates the probability of observing your results *if* the null hypothesis is true, *not* the probability the hypothesis is true. Always report alongside effect sizes.
* **Type I & Type II Errors:** Understand the risks of false positives (Type I) and false negatives (Type II) in statistical testing. Sample size impacts the likelihood of Type II errors.
* **Correlation vs. Causation:** Correlation does not equal causation. Identify potential confounding variables that might explain observed relationships. Randomized experiments (A/B tests) are best for establishing causation.
* **Curse of Dimensionality:** Adding more features doesn't always improve model performance. High dimensionality can lead to data sparsity, overfitting, and reduced model accuracy. Feature selection and dimensionality reduction techniques are important.
* **Statistical vs. Practical Significance:** Don't automatically act on statistically significant results. Consider if the effect size is meaningful in a real-world context and impacts business goals.
* **Sampling Bias:** Be aware that your dataset is rarely a perfect representation of the population. Identify potential biases in data collection that could skew results.
* **Confidence Intervals:** Report ranges (confidence intervals) alongside point estimates to communicate the uncertainty of your data. Larger intervals indicate a need for more data.
* **Interpreting P-Values:** A p-value indicates the probability of observing your results *if* the null hypothesis is true, *not* the probability the hypothesis is true. Always report alongside effect sizes.
* **Type I & Type II Errors:** Understand the risks of false positives (Type I) and false negatives (Type II) in statistical testing. Sample size impacts the likelihood of Type II errors.
* **Correlation vs. Causation:** Correlation does not equal causation. Identify potential confounding variables that might explain observed relationships. Randomized experiments (A/B tests) are best for establishing causation.
* **Curse of Dimensionality:** Adding more features doesn't always improve model performance. High dimensionality can lead to data sparsity, overfitting, and reduced model accuracy. Feature selection and dimensionality reduction techniques are important.
A visual introduction to probability and statistics, covering basic probability, compound probability, probability distributions, frequentist inference, Bayesian inference, and regression analysis. Created by Daniel Kunin and team with interactive visualizations using D3.js.
A simple explanation of the Pearson correlation coefficient with examples
A step-by-step guide to catching real anomalies without drowning in false alerts.
A neofetch-style CLI tool for GitHub statistics. Display your GitHub profile and stats in a beautiful, colorful terminal interface.
Understanding how banks use the KS statistic in loan approvals.
This article details a hands-on approach to modeling rare events in time series data using Python. It covers data exploration, defining extreme events, fitting distributions (GEV, Weibull, Gumbel), and evaluating model performance using metrics like log-likelihood, AIC, and BIC. The example uses weather data and provides code snippets for implementation.
Understanding and Implementing Brant’s Tests in Ordinal Logistic Regression with Python. This article details the proportional odds model for ordinal logistic regression, its assumptions, and methods to assess the proportional odds assumption using likelihood ratio tests and separate fits approaches, with Python implementation examples.
This discussion explores the effectiveness of simulated annealing compared to random search for optimizing a set of 16 integer parameters. The author seeks to determine if simulated annealing provides a significant advantage over random search, despite the parameter space being too large for exhaustive search. Responses suggest plotting performance over time and highlight the ability of simulated annealing to escape local optima as its main strength.
This guide walks through applications, libraries, and dependencies of causal discovery approaches using Bayesian modeling, with a step-by-step guide on creating causal networks using discrete or continuous datasets, explaining techniques and search methods like PC and Hill Climb Search, ensuring readers understand Bayesian techniques for causal discovery in specific use cases."
