### 统计代写|数据可视化代写data visualization代考|A perception of objectivity

statistics-lab™ 为您的留学生涯保驾护航 在代写数据可视化data visualization方面已经树立了自己的口碑, 保证靠谱, 高质且原创的统计Statistics代写服务。我们的专家在代写数据可视化data visualization代写方面经验极为丰富，各种代写数据可视化data visualization相关的作业也就用不着说。

• Statistical Inference 统计推断
• Statistical Computing 统计计算
• (Generalized) Linear Models 广义线性模型
• Statistical Machine Learning 统计机器学习
• Longitudinal Data Analysis 纵向数据分析
• Foundations of Data Science 数据科学基础

## 统计代写|数据可视化代写data visualization代考|A perception of objectivity

According to Anthony McCosker and Rowan Wilken (2014), data visualizations often offer a ‘fantasy of knowing’ or of ‘total knowledge’, or in Donna Haraway’s words, they claim to present a ‘God’s eye view’ (Haraway, 1988, p. $5^{81}$ ). The use of a data visualization in a newspaper article or a corporate report carries with it a rhetorical weight: the simple presence of the data visualization seems to state ‘Look, we have data. This is true’ (see Tal, Aner, \& Wansink, 2016 on data visualization’s association with truthfulness).
José van Dijck uses the term dataism to describe the ideology of big data, which is characterized by ‘a widespread belief in the objective quantification and potential tracking of all kinds of human behavior and sociality through online media technologies’ (2014, p. 198). Epistemologically, data visualizations build upon this trust in data.

We can trace many histories of society’s growing trust in numbers. The registration of data about crimes and criminals mentioned above tells of one such history, which can be traced forwards to today’s bodycams, surveillance, and biometrics (Gates, 2011). Another, parallel history is that of the transition from midwives and their home-based care of mothers and infants to the increasing medicalization of prenatal care. This story can be told as a transfer of power from women to men, but it can also be seen as a transfer of trust from humans to machines, as the increasing institutionalization of prenatal and infant care included a radical growth in the use of technology to monitor growth and health (Oppenheimer, 2013). Today, iPhone apps connect to digital scales that generate daily data visualizations of a baby’s weight (Rettberg, 2014, p. 67 ) and smart socks generate continuous visualizations of a baby’s heartbeat (Leaver, 2017).
The management of birth is one thread in this story of numbers. Another thread is the management, or perhaps rather the marketing, of instruments of death, as told by Donald Mackenzie in Inventing Accuracy: A Historical Sociology of Nuclear Missile Guidance (1993). Or we might consider the prevention of life, a thread of the story told by Michelle Murphy in The Economization of Life (2017), where she discusses how demographic models comparing population size and financial growth created programmes intended to improve the future economies of developing countries through extensive birth control and abortion programmes.

## 统计代写|数据可视化代写data visualization代考|The average as norm

Displaying data visually rather than as a table of numbers is a powerful method for finding patterns in the data. Some patterns recur in many different datasets, such as the bell-shaped curve seen in Figure 2.1, a graph showing the heights of Belgian men, which follows what is mathematically known as a normal distribution. Writing in the 1860 , Adolphe Quetelet interpreted this recurrence as evidence of a fundamental social law, and defined the central portion of the curve as ‘normal’, with those outside the normal zone seen as aberrations (1997). Sekula explains that ‘[t]hus conceived, the “average man” constituted an ideal, not only of social health, but of social stability and of beauty’ (1986, p. 22). Quetelet’s work leaned heavily upon data visualizations. He first showed his data in the form of a table, then showed it visualized, drawing conclusions from the patterns that became apparent when the numbers were shown as curves on an $x$ – and $y$-axis.

The power of visualizations to show averages and patterns contributed to the nineteenth-century privileging of the ‘norm’, or as Lennard Davis argues, a ‘generalized notion of the normal as an imperative’, where ‘the average then paradoxically becomes a kind of ideal, a position to be wished’ (Davis, 2013, p. 2). This privileging of the average is a marked break from earlier traditions that saw the ideal body, represented for instance in paintings of Venus, as something ‘mytho-poetical’, a ‘divine body’ that is ‘not attainable by a human’ (Davis, 2013, p. 2).

As it turns out, the average human doesn’t exist. Yes, that even curve shape shown in Figure $2.1$ does show up again and again when you measure almost any aspect of humans – or of most things, really. But that doesn’t mean that any individual human is ‘average’. In her book Technically Wrong (2017), Sara Wachter-Boettcher tells the story of how the adjustable seatbelt was designed. Prior to its invention, the air force planned to design cockpits that fit ‘the average pilot’-but they discovered that none of their pilots were of average size in all the ten dimensions they measured, such as height, wrist circumference, and shoulder width. Wachter-Boettcher uses this point to argue that it’s important to design technology that fits people at each extreme rather than for the average person, as the air force did by creating adjustable seats and seat belts (Wachter-Boettcher, 2017). The idea of ‘the average’ may be encouraged by data visualizations, but that doesn’t mean that it’s necessarily the most useful way of viewing the data.

## 统计代写|数据可视化代写data visualization代考|Correlation is easier than causation

Computers are extremely good at finding correlations. In fact, this is one of the mainstays of current models of deep machine learning, where software is fed ‘big data’ and works through it to find patterns. By analysing historical data, computers can find patterns that allow them to predict future behaviour. Often these predictions are eerily accurate. In some tests, AI systems do a better job at medical diagnosis than human doctors (Olson, 2018). It is wise to remember, though, that many stakeholders have a strong financial interest in convincing the general public that AI is efficient, perhaps more efficient than humans, and AI’s ability to make accurate predictions is often overstated.

Visualizations of data also prioritize correlation over causation. They show patterns and relative size or position, but it is more difficult to show

causality. Viktor Mayer-Schönberger and Kenneth Cukier argue in their book Big Data (2013) that we no longer need causality. If we can predict how likely patients are to take their medicine based on their car insurance payment history, why would we want or need to know why they don’t take their medicine, Mayer-Schönberger and Cukier ask. Obviously their payment history doesn’t cause their tendency to take or not take medicines as prescribed. But it no longer matters. Causality for them is simply hhuman intuiting’ that doesn’t deepen our insight, it is merely a ‘cognitive shortcut that gives us the illusion of insight but in reality leaves us in the dark about the world around us’ (2013, p. 64). Others are more concerned about the downplay of causality, as Wendy Chun writes: ‘Big data […] offers a form of cognitive mapping that allegedly sees all, by ignoring causes’ (2017, p. 56 ).
Different forms of representation emphasize different relationships and patterns. Quetelet’s data visualizations contributed to the idea of the average as something to be sought after, whereas earlier forms of representation, such as paintings, were well-suited to representing ideal beauty as something beyond human perfection. Data visualizations prioritize correlation. Narrative, by contrast, is a form of representation that often emphasizes causal connections. Narratives organize events in time. Some also provide causal connections between the events. E. M. Forster argues that such connections separate a story, which is just events in time (‘and then, and then’), from a plot, which adds causality. “The king died and then the queen died,” is a story. “The king died, then the queen died of grief” is a plot,’ Forster wrote (1949, p. 82). Roland Barthes, on the other hand, argued that ‘the mainspring of narrative’ is the reader’s assumption that an event that happens after another event is caused by the first event, meaning that ‘narrative would be a systematic application of the logical fallacy […] post hoc, ergo propter hoc’ (1977, p. 94). Causation is not always evident, but different forms of representation emphasize causation or correlation in different ways. Visualizations do not usually portray narratives, although this is certainly possible, as discussed by Wibke Weber and others in this volume.

## 统计代写|数据可视化代写data visualization代考|A perception of objectivity

José van Dijck 使用术语数据主义来描述大数据的意识形态，其特点是“普遍相信通过在线媒体技术对各种人类行为和社会性进行客观量化和潜在跟踪”（2014 年，第 198 页） . 从认识论上讲，数据可视化建立在对数据的这种信任之上。

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