NishMath - #statisticalanalysis

Recent discussions in statistics highlight significant concepts and applications relevant to data science. A book review explores seminal ideas and controversies in the field, focusing on key papers and historical perspectives. The review mentions Fisher's 1922 paper, which is credited with creating modern mathematical statistics, and discusses debates around hypothesis testing and Bayesian analysis.

Stephen Senn's guest post addresses the concept of "relevant significance" in statistical testing, cautioning against misinterpreting statistical significance as proof of a genuine effect. Senn points out that rejecting a null hypothesis does not necessarily mean it is false, emphasizing the importance of careful interpretation of statistical results.

Furthermore, aspiring data scientists are advised to familiarize themselves with essential statistical concepts for job interviews. These include understanding p-values, Z-scores, and outlier detection methods. A p-value is crucial for hypothesis testing, and Z-scores help identify data points that deviate significantly from the mean. These concepts form a foundation for analyzing data and drawing meaningful conclusions in data science applications.


References :

• errorstatistics.com: Stephen Senn (guest post): “Relevant significance? Be careful what you wish for”
• Towards Data Science: 5 Statistical Concepts You Need to Know Before Your Next Data Science Interview
• Xi'an's Og: Seminal ideas and controversies in Statistics [book review]
• medium.com: Statistics for Data Science and Machine Learning
• medium.com: Why Data Science Needs Statistics

Classification:

• HashTags: #Statistics #DataScience #MachineLearning
• Company: Statistics
• Target: Analysis
• Product: Statistics
• Feature: StatisticalAnalysis
• Type: Research
• Severity: Medium