Statistics for Absolute Beginners Quotes

“Clustering analysis developed originally from anthropology in 1932, before it was introduced to psychology in 1938 and was later adopted by personality psychology in 1943 for trait theory classification. Today, clustering analysis is used in data mining, information retrieval, machine learning, text mining, web analysis, marketing, medical diagnosis, and numerous other fields.”
― Oliver Theobald, Statistics for Absolute Beginners: A Plain English Introduction

“Before we proceed further, be wary that neither the null hypothesis nor the alternative hypothesis can be unequivocally proven correct within hypothesis testing. Analyzing a sample extracted from a larger population is a subset of the data, and thus, any conclusions formed about the larger population based on analyzing the sample data are considered probabilistic rather than absolute.”
― Oliver Theobald, Statistics for Absolute Beginners: A Plain English Introduction

“A high-profile example of this type of data bias appeared in Google’s “Flu Trends” program. The program, which started in 2008, intended to leverage online searches and user location monitoring to pinpoint regional flu outbreaks. Google collected and used this information to tip-off and alert health authorities in regions they identified. Over time the project failed to accurately predict flu cases due to changes in Google’s search engine algorithm. A new algorithm update in 2012 caused Google’s search engine to suggest a medical diagnosis when users searched for the terms “cough” and “fever.” Google, therefore, inserted a false bias into its results by prompting users with a cough or a fever to search for flu-related results (equivalent to a research assistant lingering over respondents’ shoulder whispering to check the “flu” box to explain their symptoms). This increased the volume of searches for flu-related terms and led Google to predict an exaggerated flu outbreak twice as severe as public health officials anticipated.”
― Oliver Theobald, Statistics for Absolute Beginners: A Plain English Introduction

“For a long period of human history, most of the world thought swans were white and black swans didn’t exist inside the confines of mother nature. The null hypothesis that swans are white was later dispelled when Dutch explorers discovered black swans in Western Australia in 1697. Prior to this discovery, “black swan” was a euphemism for “impossible” or “non-existent,” but after this finding, it morphed into a term to express a perceived impossibility that might become an eventuality and therefore disproven. In recent times, the term “black swan” has been popularized by the literary work of Nassim Taleb to explain unforeseen events such as the invention of the Internet, World War I, and the breakup of the Soviet Union.”
― Oliver Theobald, Statistics for Absolute Beginners: A Plain English Introduction

“Social desirability generates bias when respondents are inclined to present themselves in a favorable light or respond in a way that aligns with desirable social norms.”
― O. Theobald, Statistics for Absolute Beginners: A Plain English Introduction

“In hypothesis testing, the null hypothesis (H0) is assumed to be the commonly accepted fact but that is simultaneously open to contrary arguments. If there is substantial evidence to the contrary and the null hypothesis is disproved or rejected, the alternative hypothesis is accepted to explain a given phenomenon. The alternative hypothesis is expressed as Ha or H1. Intuitively, “A” represents “alternative.” The alternative hypothesis covers all possible outcomes excluding the null hypothesis.”
― O. Theobald, Statistics for Absolute Beginners: A Plain English Introduction