“The importance of stupidity in scientific research” by Martin A. Schwartz

I recently saw an old friend for the first time in many years. We had been Ph.D. students at the same time, both studying science, although in different areas. She later dropped out of graduate school, went to Harvard Law School and is now a senior lawyer for a major environmental organization. At some point, the conversation turned to why she had left graduate school. To my utter astonishment, she said it was because it made her feel stupid. After a couple of years of feeling stupid every day, she was ready to do something else.

I had thought of her as one of the brightest people I knew and her subsequent career supports that view. What she said bothered me. I kept thinking about it; sometime the next day, it hit me. Science makes me feel stupid too. It’s just that I’ve gotten used to it. So used to it, in fact, that I actively seek out new opportunities to feel stupid. I wouldn’t know what to do without that feeling. I even think it’s supposed to be this way. Let me explain.

For almost all of us, one of the reasons that we liked science in high school and college is that we were good at it. That can’t be the only reason – fascination with understanding the physical world and an emotional need to discover new things has to enter into it too. But high-school and college science means taking courses, and doing well in courses means getting the right answers on tests. If you know those answers, you do well and get to feel smart.

A Ph.D., in which you have to do a research project, is a whole different thing. For me, it was a daunting task. How could I possibly frame the questions that would lead to significant discoveries; design and interpret an experiment so that the conclusions were absolutely convincing; foresee difficulties and see ways around them, or, failing that, solve them when they occurred? My Ph.D. project was somewhat interdisciplinary and, for a while, whenever I ran into a problem, I pestered the faculty in my department who were experts in the various disciplines that I needed. I remember the day when Henry Taube (who won the Nobel Prize two years later) told me he didn’t know how to solve the problem I was having in his area. I was a third-year graduate student and I figured that Taube knew about 1000 times more than I did (conservative estimate). If he didn’t have the answer, nobody did.

That’s when it hit me: nobody did. That’s why it was a research problem. And being my research problem, it was up to me to solve. Once I faced that fact, I solved the problem in a couple of days. (It wasn’t really very hard; I just had to try a few things.) The crucial lesson was that the scope of things I didn’t know wasn’t merely vast; it was, for all practical purposes, infinite. That realization, instead of being discouraging, was liberating. If our ignorance is infinite, the only possible course of action is to muddle through as best we can.

I’d like to suggest that our Ph.D. programs often do students a disservice in two ways. First, I don’t think students are made to understand how hard it is to do research. And how very, very hard it is to do important research. It’s a lot harder than taking even very demanding courses. What makes it difficult is that research is immersion in the unknown. We just don’t know what we’re doing. We can’t be sure whether we’re asking the right question or doing the right experiment until we get the answer or the result. Admittedly, science is made harder by competition for grants and space in top journals. But apart from all of that, doing significant research is intrinsically hard and changing departmental, institutional or national policies will not succeed in lessening its intrinsic difficulty.

Second, we don’t do a good enough job of teaching our students how to be productively stupid – that is, if we don’t feel stupid it means we’re not really trying. I’m not talking about `relative stupidity’, in which the other students in the class actually read the material, think about it and ace the exam, whereas you don’t. I’m also not talking about bright people who might be working in areas that don’t match their talents. Science involves confronting our `absolute stupidity’. That kind of stupidity is an existential fact, inherent in our efforts to push our way into the unknown. Preliminary and thesis exams have the right idea when the faculty committee pushes until the student starts getting the answers wrong or gives up and says, `I don’t know’. The point of the exam isn’t to see if the student gets all the answers right. If they do, it’s the faculty who failed the exam. The point is to identify the student’s weaknesses, partly to see where they need to invest some effort and partly to see whether the student’s knowledge fails at a sufficiently high level that they are ready to take on a research project.

Productive stupidity means being ignorant by choice. Focusing on important questions puts us in the awkward position of being ignorant. One of the beautiful things about science is that it allows us to bumble along, getting it wrong time after time, and feel perfectly fine as long as we learn something each time. No doubt, this can be difficult for students who are accustomed to getting the answers right. No doubt, reasonable levels of confidence and emotional resilience help, but I think scientific education might do more to ease what is a very big transition: from learning what other people once discovered to making your own discoveries. The more comfortable we become with being stupid, the deeper we will wade into the unknown and the more likely we are to make big discoveries.

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“An individual’s level of personal well-being is strongly related to the level of wealth of the household in which they live”

“Relationship between Wealth, Income and Personal Well-being, July 2011 to June 2012”

Summary
This article uses data from the Wealth and Assets Survey (WAS) for July 2011 to June 2012
which, for the first time, included measures of personal well-being. It describes the results of
regression analysis considering the relationships between the total wealth or total income of
the households in which individuals live and their personal well-being. Regression analysis is
a statistical technique which was used to analyse variation in well-being outcomes by specific
characteristics and circumstances of individuals while holding all other characteristics equal.
This allows for a better understanding of what matters most to an individual’s personal well-being
compared to analysis when different factors are considered separately.
Main points
• An individual’s level of personal well-being is strongly related to the level of wealth of the
household in which they live. Life satisfaction, sense of worth and happiness are higher, and
anxiety less, as the level of household wealth increases.
• The levels of household income are less strongly related, with relationships found only with life
satisfaction and sense of worth.
• The net financial wealth of the household appears to be the type of wealth most strongly
associated with personal well-being. In particular, life satisfaction will be higher in households
with greater net financial wealth.
• Levels of property wealth and private pension wealth were not found to be related levels of
personal well-being.

Source

Why philosophy matters to your research project

This Slavoj Zizek video illustrates very well why and for what we need philosophy in our research design. Simply because philosophy helps us to formulate the right research question. Because there are not only wrong answers but also wrong questions.

Example of explanatory research by mean secondary data

A referendum to limit migration from European Union countries took place on 10th February of 2014 in Switzerland. In the score of such event, Alexandre Afonso (2014) and Paul Haydon (2014) did a simple analysis of correlation between the share of migrants population per canton and the share of yes to anti-immigration initiative, based on the results of the referendum. The research question that lies beneath these analysis might be “is there a relationship between the share of migrant population in a given community and the way migration is seen by its members. Interestingly both the graphic and map bellow show that wherever there is less number of immigrants, the rejection of immigrants is greater. It is a clear example of explanatory research, where the main objective is identifying the existence between two or more variables. By the way, Swiss voters narrowly back referendum curbing immigration.

The results of this case, also arose multiples new questions on how public opinion is build. Is there a real problem with immigrants or rather certain media shape deliberately population opinion?

map refer

migrants

Reference list

Haydon, Paul (@Paul_Haydon) (2014) “Map of who voted how in Swiss referendum. Areas with fewest immigrants most anti-immigration pic.twitter.com/uZqicWyvC4” 9th of February, 2014, 6:34 PM

Foulkes, Imogen (2014, February 11). Swiss immigration: 50.3% back quotas, final results show. BBC News. Retrieved from http://www.bbc.co.uk/news/world-europe-26108597

 Afonso, Alexandre @alexandreafonso (2014) “Relationship between share of migrants per canton and share of yes to anti-immigration initiative pic.twitter.com/MEqgN6a4Ww” 9th of February, 2014, 6:31 PM

Qualitative methods for market research. The subject.

After providing in the previous two posts a brief definition of both terms “qualitative method” and “market research“, we are in a position to clarify what Qualitative methods of market research subject is about (see about for further details on this blog). The main objective of the subject is learning how to collect text (and images) information systematically in order to understand the relation between buyers and sellers of a specific product or service that occurs or might occur in the future in a part of the worldMore specifically, the subject will aim the managing of the below qualitative research techniques (as well as its respective emerging online variant)

  1. In-depth Interviews
  2. Narratives
  3. Focus groups
  4. Verbal data
  5. Participant observation and ethnography
  6. Visual data: photography, film and video

Furthermore, a number of secondary objectives must be pointed out. Apart from the collection of information itself, it is necessary to emphasize the importance of the research process as a whole. In other words, you as researcher may manage the above techniques but it would be pointless if you are not aware of a number of steps that all researchers must bear in mind when developing a research project and that forms what is called “research process”. This process, that will be addressed in future posts, goes from the mere formulation of the research question to the final presentation of the results.

Finally, ethics of research, origin and history of market research as well a brief theoretical approaches overview complement the secondary objectives of this subject.

Below you can find the main references taken to the production of the material for the subject´s content.

Flick, U. (2009). An introduction to qualitative research. Sage Publications Limited.

Gummesson, E. (1999). Qualitative methods in management research. Sage Publications, Incorporated.

Ibáñez, J. (1979). Más allá de la sociología: El Grupo de Discusión: teoría y crítica. Siglo XXI de España Editores.

Lewis, Philip, Mark NK Saunders, and Adrian Thornhill. Research methods for business students. Pearson, 2009.

Martínez, P., & Rodríguez, P. M. (2008). Cualitativa-mente. ESIC Editorial.

Mella, O. (1998). Naturaleza y orientaciones teórico-metodológicas de la investigación cualitativa. Santiago: CIDE, 51.

Silverman, D. (2011). Interpreting qualitative data. Sage Publications Limited.

What are the most common weaknesses in formulating a research proposal?

Assuming as a good research proposal the points suggested in the previous post What is a good research? (excepts those on “are you fascinated with it” and “does it match your career goals” since it depends on you) and after having examined around 17 proposals, here you can see the results obtained from a quantitative approach. Please, note that every category has been rated on a scale from 1 to 3 where 1=low compliance level and 3 high compliance level. Therefore, values ​​close to 3 indicate a high compliance while values close to 1 rather low.

ranking 2

Working at theory level when formulating a research

Although the main role of theory is played in more advanced research stages, such as review literature or adopting a research philosophy and approach, the truth is that its importance begins earlier than this: “it should inform your definition of research questions and objectives” (Saunders, 2009). The word theory is probably one of the most misused and misunderstood in education” (Saunders, 2009). What is in texbook is usually seen as “theories”, whereas what is happening in the “real world” is practice. Equally, in the previous post under the title #thenatureofresearch was highlighted that many managers still base their decision making on personal experience rather than in research.

But theory consists of a relationship between cause and effect that it is not only present in the research world but also in our daily life. We all attempt to solve the daily problems that we have to face up in a similar way as scientist. We all constantly make hypotheses and check them according to our experience. Why do you usually take the bus number 3 if the 12, 22 and 48 also go to your destination?. Perhaps because according to your experience, the bus 12 is the least crowded. This schemata that you have in your mind derive in a theory, in your own theory. In doing your own research it works in much the same way. Following the example given in How to turn a research idea into a research question when #formulatingaresearch about unemployment in European Union, you may develop your own theory after identify, for instance, a great relationship between unemployment rate and Gross Domestic Product evolution.

But coming back to the question that head this post, why is theory important in formulating a research, you must bear in mind that before setting such research questions as Why northern European countries registered higher unemployment rate? You must be aware of whether this question has already been answer in previous researches. Would you avoid looking up the buses bulletin board to check the prices that most suit you? In this way you will save the money and time require to check it by yourself. In much the same way, a preliminary review of literature will contribute to know whether your research question has been answer and whether you should formulate a different and not yet answered question such as, How affect unemployment rate in the different European countries in terms of suicide?, just for giving an example. In other words, to create new knowledge and to contribute to see further in your area of knowledge, you must account for the works created by other researchers.

And this idea leads us to the very famous sentence in the science world: “Stand on the shoulders of giants“. To illustrate the importance this idea, below you can see a video of a very nice tradition that take place in Terrasa (CataloniaSpain) so called “Minyons de Terrassa” In this video, a student graduate would probably be represented by the little girl who reach the top while the rest would be the preivous researchers who has worked on your topic. Not every year the little girl achieves the top. Hopefully you will.

watch?v=jHmd2G39VzU