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 simple explanation of the Pearson correlation coefficient with examples
A step-by-step guide to catching real anomalies without drowning in false alerts.
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.
Explores the role of conditional probability in understanding events and Bayes' theorem, with examples in regression analysis and everyday scenarios, demonstrating how our biological tissue runs probabilistic machinery.
This article explains the PCA algorithm and its implementation in Python. It covers key concepts such as Dimensionality Reduction, eigenvectors, and eigenvalues. The tutorial aims to provide a solid understanding of the algorithm's inner workings and its application for dealing with high-dimensional data and the curse of dimensionality.
‘I’ve been to Bali too’ (and I will be going back): are terrorist shocks to Bali’s tourist arrivals permanent or transitory?,”
sub-populations that have different variabilities from others. Here "variability" could be quantified by the variance or any other measure of statistical dispersion
