Three kinds of lies: statistics, jargon, and social consensus

Before 1938 there were no federal regulations in place to ensure the safety of cosmetics sold throughout the United States, and unbeknownst to consumers, certain beauty products contained toxic ingredients. The repercussions of this were felt in 1933 when Lash Lure, an eyebrow and eyelash cosmetic dye, resulted in serious medical injury for numerous customers. Unfortunately, the dye consisted of a component of coal tar that led to abscesses and blisters, as well as ulcers on the eyes, eyelids, and the faces of many of its users. Several individuals went blind, and in one case the reaction to Lash Lure was so severe that it induced an infection resulting in death.1 As a consequence, eyelash dyes were banned in several states, and in 1938 the American Food, Drug and Cosmetic Act was introduced, which set standards for allowable cosmetic ingredients. To this day, the Food and Drug Administration (FDA) continues to enforce what substances can and cannot be included in cosmetics. However, the FDA has often been unsuccessful in policing deceptive advertising claims made by the cosmetic industry.2 Such claims include dubious assertions about the results of clinical testing, questionable descriptions regarding engineered formulas, alleging the use of scientifically proven substances, along with vague descriptions about the research behind the beautifying actions of numerous cosmetic products.3 Within these assertions it is common to find technical jargon relating to how various serums or creams operate, as well as statistics concerning mathematical measures outlining how cosmetics will statistically improve one’s looks. In this way, the advertisements bearing such statements often express characteristics associated with the persuasive cue Statistics and Technical Jargon, which can serve as a heuristic indicants of a messenger’s credibility.

Numbers, jargon and disruptions

Statistics

In his popular text on persuasion, Robert Levine explains, “The inclusion of statistics, even when they’re meaningless, can signal expertise.”4 Statistics can act as a marker of the communicator’s know-how by making use of

Three kinds of Iles 179 people’s innumeracy. This innumeracy has been described as an audience’s “functional incompetence to understand and argue back to a statistic, and to draw accurate meaning from, and criticize, a statistical argument about the real world.” Since many people suffer from the inability to quickly and completely comprehend numerical statistics or notions of probability, they tend to yield to the computational deftness and abilities of communicators. Accordingly, “Many people confronted by a marketer’s numbers or statistics suspend otherwise natural skepticism in favor of regressive obedience to argument by authority.”5 For that reason, the use of statistics can serve as a marker of expertise, and the inability to fully grasp the nuances of statistical information can lead audiences to peripherally depend upon credibility indicators.6 As one study concluded, statistics were found to cause people “to rely on a peripheral cue - the character of the communicator - as a basis for judgment, so that an expert communicator induced greater persuasion than did a source of lower expertise.”7

Furthermore, when statistical information is represented in the form of a graphic, such as a pie chart displaying poll results, it functions as a type of persuasive mental shortcut, especially for audiences who are not particularly well informed about a communicated topic.8 In this way, statistics can enhance communications over and above the rigor of an argument, for as John Allen Paulos has stated wryly, mathematics “is the quintessential way to make impressive-sounding claims which are devoid of factual content.”9 At the same time, statistics seem to be especially persuasive when a message delivers ideas that conform to an audience’s preconceptions. That is, when communications relay statistics validating ideas congruent with recipients’ own partialities, including the attitudes associated with an audience member’s self-defining reference groups, then numerical information appears particularly effective in bolstering those preexisting assumptions.10

Technical jargon

The use of technical jargon, which is complex language that may be incomprehensible to non-specialists, has been found to operate in a similar persuasive manner as statistics. As with the presentation of statistical information, studies suggest that there is a complementary relationship between the use of elaborate, rather indecipherable, terminology and the perceived expertise of a communicator. For instance, when a communicator uses highly specialized scientific jargon, audiences tend to subsequently rely upon the broadcaster’s credentials in determining the persuasiveness of the message itself. If both jargon and credentials are present in tandem, the message is more frequently deemed to be convincing, as the complex language appears to catalyze attention to, and the potential influence of, prestigious accreditations. However, if jargon is unaccompanied by impressive credentials, then such complex language becomes less persuasively compelling. In this sense, jargon triggers persuasion via Messenger Credibility while it is also dependent upon it.11 When considering both jargon and statistics, it is also of value to address the Disrupt-then-Reframe persuasion technique, as it can incorporate the deliberate use of technical language and confounding numbers.

Disnipt-then-reframe

This influence method involves using a communicative disruption by conveying something in an unexpected way, followed by a reframing statement that includes a persuasive message. With regard to disruption, it has been found that when panhandlers asked passersby for a specific, somewhat unusual value of money, such as 17y or 37y, as opposed to the more usual request for a single coin denomination or simply for some spare change, people were 60% more likely to give money.12 Likewise, Davis and Knowles studied the Disrupt-then-Reframe technique via an experiment which attempted to sell packages of note cards in support of a charity to potential customers. The test first involved a “small disruption,” in which the item’s price was stated in pennies rather than dollars, followed by “direct reframing” through the declaration that this price was a bargain.13 The routine significantly increased sales, and the disruption component was theorized to lower the processing levels of individuals, and then the reframing acted as a peripheral cue. Research suggests that using this technique causes persuasive requests to be accepted one and a half to two times more often than if it had not been employed.14 Additionally, and with regard to technical jargon, it has been found that complex, specialized language can serve as a mechanism of message disruption in much the same manner as asking for nonroutine amounts of money. For instance, a sales pitch that first includes complicated technical information about a product, followed by a reframing explanation of why such features are beneficial, can result in increased preferences for the vended item.15

In this chapter technical jargon will be identified as the use of specialized, complex language and scientific nomenclature, such as the use of Latin taxonomical species classifications, and specialist terms requiring an education in specific fields of study. Technical drawings and elaborate scientific charts will also be considered as articulations of this cue. Statistics are distinguished as the use of numerical data in support of an argument, such as reports of mathematical data and percentages, with particular attention given to graphical representations of such statistical information. The frequency values of Statistics and Technical Jargon cues throughout Evolution Wars communications are detailed in Figure 6.1. It is also of note that in Evolution Wars media statistics are frequently used to demonstrate that a significant majority of a population supports either Darwin-skepticism or evolutionary theory. In this way, the use of numerical data coincides with an important facet of the persuasive cue aggregately described here as Social Consensus. This persuasion element includes the influential message attributes identified as Multiple Sources, Social Proof, and Underdog Effects,

Recurrence Rates of Statistics and Technical Jargon

Figure 6.1 Recurrence Rates of Statistics and Technical Jargon

which in their own way kindle the idea that an important number of individuals endorse a marketed product or advertised opinion. For that reason, though Social Consensus differs from Statistics and Technical Jargon in how it operates as a persuasive device, its frequent appearance alongside statistics warrants its coverage here.

 
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