Damned Lies & Statistics discusses a number of factors that impact the ways quantitative research is conducted, disseminated, and ultimately interpreted by researchers and consumers (e.g., readers of journal and newspaper articles). In what ways are the arguments raised in this book "bigger" than the simple misuse and/or misunderstanding of statistics by researchers and consumers? How might the arguments raised in the book connect with bigger themes/issues pertaining to the research (both quantitative and qualitative) process and the dissemination of knowledge? Discuss.
Statistics of Social issues are a reflection of the activities of individuals that identify with, name, describe, gauge, and promote. An extensively known name changes a condition that we take for granted into a situation we regard as disturbing and worth gauging. Simply put, statistics are regarded as a product of social activity. In addition, statistics are social constructs: it is through them that the world is made meaningful. We, however, treat socially constructed numbers like pieces of undeniable truth. The outcome is that statistical instruction tends to downplay thoughts of how real-life statistics come into being. Critics of statistics frequently reject this as exaggerated proof. There exists no means of avoiding uncertainty in the ultimate decision as to which significance level is actually "significant." As such, the selection of some significance level, where the findings shall be declined as being invalid, is random. Practically, the ultimate decision normally relies on whether the result was foretold a priori, or simply discovered post hoc during several evaluations and comparisons conducted on the set of data, overall amount of steady supportive discussions and qualitative analysis, and on "traditions" present in the respective research field.
2) What does the book suggest to you regarding your responsibilities as a potential researcher/knowledge producer (and yes, your dissertation still counts as "knowledge production")? Reflect on this and discuss.
Knowledge is actually considered as factual when it is backed by proof and when we have tremendous trust in its accuracy. What is referred to as "hard fact" is data backed up by strong, compelling proof; this refers to proof that is undeniable regardless of how we evaluate or examine it. Facts could always be doubted, however, they survive even under doubt. How did people stumble upon this data? How did they understand it? The more adequate the responses to these questions are, the "harder" the facts.
Even though at times we consider social statistics as being straightforward, we need to inquire the context of creation of those figures. Bear in mind that individuals promoting social issues wish to influence others, and they utilize statistics in order to make their arguments more convincing. The means via which people generate statistics may be frequently faulty; their figures might be a little more than guesses; or the numbers might be a product of bad definitions, faulty measurements, or poor sampling. The above are only the four basic ways of creating poor social statistics.
Researchers face the task of basing their statistics on more than guessing by establishing how the statistical data was obtained. Secondly, researchers should make sure that their statistics are founded on clear, sensible definition. All researchers ought to describe their statistics. Thirdly, researchers are also faced with the task of making sure that statistics are founded on clear, sensible measures. Lastly, researchers should base their statistics on appropriate examples; the techniques applied for choosing the representative source of the data ought to be clarified. In summary, researchers are faced with the task of guaranteeing high-quality statistics where they provide the suitable descriptions, measurements, as well as sampling techniques utilized to get the statistical figure presented.
3) Describe how you see your role, as an educated person, where it comes to helping those without your background in statistics to understand research findings.
The educated in the community have a role to help the populations understand and appreciate the inevitable limitations that affect all statistics. The solution to the issue of poor statistics is not to overlook all statistics, or to presume that all numbers are false. Some statistics are poor while others are quite good, and we require statistics to speak of social issues sensibly. Therefore, the solution is not to give up on statistics, rather to become better judges of the figures we come across. We ought to seriously think about statistics. The individuals that present social statistics do so for various reasons. Statistics are devices used for specific reasons. Seriously thinking about statists calls for knowing their place in the society. Almost all social activists and studies use statistics, when seeking to substantiate their perceived evaluation of a social construct. To understand and use statistics responsibly, we require more than just a checklist of ordinary mistakes. Statistics need be approached in a thoughtful manner. This can be difficult to do, mainly since many people in our society consider statistics like fetishes. We may refer to this as mind-set of the 'Awestruck'. The 'awestruck' recognize that they do not always comprehend the statistics they hear; however, this does not disturb them. In any case, who can expect to comprehend magical numbers? The deferential fatalism of the awestruck is not considerate; it is a means of evading consideration. We require another approach. One option is to assess the statistics provided critically. Being critical does not imply being aggressive or negative. They approach statistics thoughtfully; they evade the extremes of both cynical refusal and naive acceptance of the figures they come across. Rather, the critical try to assess numbers, to differentiate between good and poor statistics.
4) The author makes the claim that the ways in which things are measured "counts" when it comes to conducting statistical analyses and, ultimately, the way that statistics are interpreted. Reflect on and discuss what you perceive to be the main points the author addresses in relation to this topic. What insights did you arrive at?
Best recognizes different kinds of figures which shape our regard for public concerns: missing numbers are significant but ignored; confusing numbers confuse when they should inform; authoritative numbers command respect they do not deserve; scary numbers play to our worries regarding the present and the future; contentious numbers turns into the focus of data fights and initiate wars, and magical numbers assure simple, unrealistic solutions to difficult issues. The author's utilization of relevant, socially significant examples gives proof of life-altering repercussions of understanding/misunderstanding statistical data. He discredits statistical measures, to explaining in straightforward text, how choices are made regarding what to count and what not to count, about which presumptions are made, and which numbers are presented to us.
It is crucial to have the old saying in mind regarding measuring oranges and apples in establishing what the statistics are actually measuring. Majority of individuals argue that one cannot compare oranges and apples. This is true and false; it all relies on what is being measured (texture, color, acidity). In utilizing statistics as proof, the function of the user is to establish what is actually being measured. If "Nutritional Value of Oranges" is for instance the topic, statistics providing proof that oranges are nothing like apples might be measuring the incorrect things. Best statistics provide different kinds of figures that influence our reasoning towards public concerns. Informative, entertaining, and quite timely, this book provides a foundation for critical thinking regarding the figures we come across and a reminder that people count when it comes to the news.
5) How does the author address the issue of "sampling" in the book? Reflect on and discuss what you perceive to be the main points. What insights did you arrive at?
Sample surveys are a suitable source of unsuitable and spin statistics. Samples that are quite tiny, prejudiced or unrepresentative, selective and leading questions utilized by the commissioning are examples of how this comes about. In as much as social statistics might be pretty good, they are in no way perfect. Each statistic is a means of summarizing difficult data into somewhat simple figures. Some of the difficulty in presenting some of the data is overcome when statistics are utilized. At the same time, however, some finer points may be left under- or un-represented.
It is important to know that this is an unavoidable drawback of statistics. In addition, it is crucial to understand that each statistic is a product of choices; the choice between describing a group narrowly or broadly, the sample choice. Individuals select samples for all kinds of reasons: They maybe want to stress a certain feature of an issue; maybe it is simpler and less expensive to collect data in a certain manner- many factors could come into play. Every statistic is simple a compromise amidst choices. This implies that every definition (and, thereby, every sample and measurement) most likely has drawbacks and could be criticized. Also, nearly all claims regarding social issues entail generalizing from a sample of cases. We ought to ask how much trust we need to have in these generalizations, and the response to this question shall rely on the…