{"id":91,"date":"2021-01-25T00:57:29","date_gmt":"2021-01-25T00:57:29","guid":{"rendered":"https:\/\/courses.lumenlearning.com\/suny-hccc-generalscience\/?post_type=chapter&p=91"},"modified":"2021-01-25T00:57:29","modified_gmt":"2021-01-25T00:57:29","slug":"7-data-statistics","status":"publish","type":"chapter","link":"https:\/\/courses.lumenlearning.com\/suny-hccc-generalscience\/chapter\/7-data-statistics\/","title":{"raw":"7. Data: Statistics","rendered":"7. Data: Statistics"},"content":{"raw":"
\r\n\r\nModern science is often based on statements of statistical significance<\/a> and probability<\/a>. For example: 1) studies have shown that the probability of developing lung cancer is almost 20 times greater in cigarette smokers compared to nonsmokers (ACS, 2004); 2) there is a significant likelihood of a catastrophic meteorite impact on Earth sometime in the next 200,000 years (Bland, 2005); and 3) first-born male children exhibit IQ test scores that are 2.82 points higher than second-born males, a difference that is significant at the 95% confidence level<\/a> (Kristensen & Bjerkedal, 2007). But why do scientists speak in terms that seem obscure? If cigarette smoking causes lung cancer, why not simply say so? If we should immediately establish a colony on the moon to escape extraterrestrial disaster, why not inform people? And if older children are smarter than their younger siblings, why not let them know?\r\n\r\nThe reason is that none of these latter statements accurately reflects the data<\/a>. Scientific data rarely lead to absolute conclusions. Not all smokers die from lung cancer \u2013 some smokers decide to quit, thus reducing their risk, some smokers may die prematurely from cardiovascular or diseases other than lung cancer, and some smokers may simply never contract the disease. All data exhibit variability, and it is the role of statistics<\/a> to quantify<\/a> this variability and allow scientists to make more accurate statements about their data.\r\n
\"dive\"
Figure 1: The field of statistics has its roots in calculations of the probable outcomes of games of chance.<\/figcaption><\/figure>\r\nA common misconception is that statistics<\/a> provide a measure of proof that something is true, but they actually do no such thing. Instead, statistics<\/a> provide a measure of the probability<\/a> of observing a certain result. This is a critical distinction. For example, the American Cancer Society has conducted several massive studies of cancer in an effort to make statements about the risks of the disease in US citizens. Cancer Prevention Study I enrolled approximately 1 million people between 1959 and 1960, and Cancer Prevention Study II was even larger, enrolling 1.2 million people in 1982. Both of these studies found much higher rates of lung cancer among cigarette smokers compared to nonsmokers, however, not all individuals who smoked contracted lung cancer (and, in fact, some nonsmokers did contract lung cancer). Thus, the development of lung cancer is a probability-based event, not a simple cause-and-effect relationship.\r\n\r\nStatistical techniques allow scientists to put numbers to this probability<\/a>, moving from a statement like \"If you smoke cigarettes, you are more likely to develop lung cancer\" to the one that started this module: \"The probability of developing lung cancer is almost 20 times greater in cigarette smokers compared to nonsmokers.\" The quantification of probability offered by statistics<\/a> is a powerful tool used widely throughout science, but it is frequently misunderstood.\r\n
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Comprehension Checkpoint<\/p>\r\n

Statistics can<\/p>\r\n\r\n

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