You might guess based on the title alone that the source of my irritation with anecdotes as data is that, of course, anecdotes are not data! Data are, like, numbers, right? And they have to be derived some kind of representative random sample and can be plugged into inferential computations like regression analyses and then we get stats that tell us things about the populations that the samples represent within a margin of statistical confidence, right? Because, as we all know, "the plural of anecdote is not data", right?
Oh, I mean, I get it. There's a painful lack of statistical knowledge out there, considering how much weight we put on quantitative research findings for big serious decision making.* And logic skills lack as well, with people making all kinds of absurd conclusions that the available data do not support, the notorious one being "Well, my experience is X therefore all experiences must be X", so vigilance against those kinds of silly assertions is important. But quantitative data are subject to the exact same kinds of logical fallacies in terms of drawing conclusions (Satoshi Kanazawa, anyone?), and have the unfortunate side effect of operating under the false authority of numbers and their halo effect of objectivity.
I often run into the apparently firmly-rooted assumption that numerical data are inherently more objective and therefore more trustworthy than non-numerical data**, which really isn't the case. For one, data collection methods of any kind can skew what gets recorded and what doesn't, and those methods are chosen by researchers with personal biases and limitations. People also tend to over-look the role of interpretation, which is the step that comes after data collection and analysis (i.e., drawing conclusions) and which actually forms the final product of the research that gets disseminated. I mean, very, very, very, very rarely does anyone ask to look at your raw data, and although in research publications you generally provide your key analyses and their results, these almost never make it into media-reported results (and sometimes your actual interpretations don't quite make it to that stage either - see PHD's Science News Cycle). With the exception of a key stat here and there to back up your conclusions, what quantitative research actually delivers to the public is not a wholesomely objective package of pure and irrefutable data, but an (hopefully) informed, rational, logical, realistic, but nonetheless fallible and ultimately subjective interpretation of said data.
So that's a snippet of my argument about why quantitative research is not inherently superior to qualitative research (which, as it happens, does have structure and best practices and rules which are meant to be followed to guide data collection, analysis, and interpretation, just as quantitative research has, and is just as subject to people ignoring or misunderstanding those rules and guidelines as in quantitative research), but what really bothers me about scorn for qualitative accounts (as opposed to scorn for faulty reasoning), especially in a social justice context, goes back to the false authority of numbers and the lack of respect for story-telling.
Stories are a powerful thing. I would go so far as to argue that stories are the basic unit of meaning from a human perspective - morphemes, words, sentences, paragraphs, they don't really carry that much clout without a narrative structure to give them purpose. Never mind the that stories form the heart of most of our entertainment industries - books, TV, movies, video games, hell, even celebrity stalking and reality television. In my undergraduate psychology education, as a lapsed creative writer I was fascinated by all the unexpected ways that narrative came into the discussion, in clinical and cognitive psychology especially. Stories are not just created and studied, they are also tools of research themselves - there are a number of academic fields where qualitative and narrative-oriented research paradigms predominate, including academic feminist research and in a number of Indigenous research methodologies.*** My father, a lawyer and the man who introduced me to my love and critical analysis of books and film, always recommended that the best way to win a case was to tell the best story, the one people wanted to believe, based on the available facts. Stories are how we entertain, celebrate, communicate, persuade, manipulate, teach, remember, and rebuke. Quite a bit of social justice activism is fighting to tell stories that are being suppressed and ignored, or pushing back against stories with too much sway.
Stories are a dangerous thing, because they are so powerful. Chimamanda Adichie's TED talk, The Danger of a Single Story (video with transcript), discusses this very well. An excerpt (@ 3:56 and @ 4:57):
I was startled. It had not occurred to me that anybody in his family could actually make something. All I had heard about them is how poor they were, so that it had become impossible for me to see them as anything else but poor. Their poverty was my single story of them.Even a legitimately truthful story told based on real knowledge can do damage if it is used to explain and describe what is it is too narrow and simple to represent. Again, its not the data that are the problem, but their interpretation. Interpretation is a story of its own kind - a story about the data and what they mean - and convincing people to accept your version of a story over a competing one is an exercise in power, in which the kind of power derived of socially-organized hierarchies plays a role.
Her default position toward me, as an African, was a kind of patronizing, well-meaning, pity. My roommate had a single story of Africa. A single story of catastrophe. In this single story there was no possibility of Africans being similar to her, in any way. No possibility of feelings more complex than pity. No possibility of a connection as human equals.
More from Adichie (@ 9:14):
It is impossible to talk about the single story without talking about power. There is a word, an Igbo word, that I think about whenever I think about the power structures of the world, and it is "nkali." It's a noun that loosely translates to "to be greater than another." Like our economic and political worlds, stories too are defined by the principle of nkali. How they are told, who tells them, when they're told, how many stories are told, are really dependent on power.So when someone says, "My experience is X, therefore all experiences are X", my response is not, "A-ha! Your experiences do not constitute a randomly-generated representative sample of all experiences, thus no conclusions can be drawn from them because they are invalid data," because to do that means that I am derogating the value of all stories and story-telling. Instead, I think, "A-ha! You are employing faulty logic by failing to recognize the lack of universality in your own experiences," and, depending on the situation, "A-ha! You are attempting to leverage your social power to promote your own story over the story of another."
Power is the ability not just to tell the story of another person, but to make it the definitive story of that person.
Data are data are data. Stories, while they can be deceitful and damaging, can also be liberatory and illuminating. What we need to hold accountable are the methods, biases, and interests of the people who collect and interpret data, rather than discount in totality the non-numerical in favour of the numerical.
(One final note, I actually have nothing against the word "anecdata" itself - I think it's a rather adorable portmanteau that I keep hoping to see crop up in academic circles. My issue is with the context in which is used perjoratively to refer to qualitative accounts as pseudo-data.)
*Not always, though - see Kai Nagata's piece on media and the Canadian government's war on science.
**I've got to be careful how I phrase some of this, because there's actually a difference between qualitative data in the context of quantitative research (this would be the categorical type of data - what is your gender, your nationality, your favourite colour, etc. - which can be coded numerically and statistically analyzed within certain restrictions but is not innately numerical) and the kind of data collected for qualitative research, generally through interviews, observations, document review, etc., and which is not traditionally statistically analyzed because it is both non-numerical and gathered through non-statistical sampling (although it may be coded for organizational purposes - e.g., thematic analysis). Qualitative research has a truly different character and purpose than does quantitative research, which is why people experienced in one can sometimes fail to appreciate the benefits of the other if they are trying to compare them directly, although fortunately multi-methodological approaches are taking off in popularity.
***This is important because one of the drawbacks of statistical, quantitative research is that it cannot always address very sensitive, complex, contextual questions about issues, especially where statistically valid samples are impractical or impossible (i.e., the experiences of people who are numerically underrepresented). It's a big, cumbersome jackhammer of a tool that can lose a lot of fossil in attempts to excavate answers because of the necessity of at least some degree of reductionism in order to make comparisons across individuals and groups. One might argue that qualitative research can be cumbersome in its own way, by leaving in too much context and limiting direct generalizability, hence the desirability of mixed methods or at least research fields that embrace both.