How to evaluate the expert knowledge?

¿Cómo evaluar el conocimiento experto?Sometimes, we find ourselves faced with the question of whether we should trust or not in certain scientific statements. And it is not surprising. To measure that increases the volume of information, reduces our ability to assimilate it, and this is especially true in science, where en sometimes you need a specialized knowledge prior to understand or assess according to what news or statements. The issue is important: let us think that our beliefs can determine our actions, on issues as important as nuclear energy, euthanasia, genetic modification,… So, how can we evaluate the scientific knowledge to the expert without being the expert?

There is an easy answer to this question: we can rely on the experts, without attempting to evaluate the knowledge they produce. However, as we’re told by Jerry Cederblom and David Paulsen in his work Critical reasoning (2005), this position has three major problems:

1. Determine who are the experts that we should trust to each problem. Cederblom and Paulsen used the example of nuclear energy: can we trust in a physical if we discuss the process of nuclear, but not necessarily if we consider other aspects (economy, environment,…).

2. The experts can not agree on a particular issue. It often happens that scientists value differently the same studies, because they interpret the data differently, or because keeping the other assumptions (about the assumptions in a few moments): what expert should we believe then?

3. If we delegate entirely to the experts, how could we control its influence on society? Put another way, if we refuse to form an opinion on certain topics, we are exposed to that experts exercise a disproportionate influence in our lives: are we willing to believe everything that they tell us.

Seen as seen, the more we would be evaluating the expert knowledge, although we ourselves do not become experts. But, how do you do it then? Janet D. Stemwedel, on his blog Doing Good Science, offers us a reflection that seems to me very successful.

Us says Stemwedel that the empirical sciences are based on data and, although details about these data can vary greatly between scientific disciplines, there are common elements in the patterns of reasoning that scientists use to contrast their theories with the data.

Thus, although we cannot directly assess the raw data, or the method used for obtaining them, we can assess these patterns of reasoning once you understand the basics of the scientific method. In this way, we would be able to evaluate the way in which the data are used to support or refute the hypothesis, we could detect logical fallacies. in the argument,… As it reminds us of the author, these are the assessments that a person exercised in critical thinking could take place, even if non-experts.

Let’s be specific then: what should we evaluate when we face the expert knowledge? According to Stemwedel, the following:

  • What is the hypothesis that is defended
  • What you expect to observe the expert if the hypothesis is true (and the reverse: what do you expect to observe the expert if the hypothesis is false)
  • What is, in effect, what the expert observed in the data
  • What the expert says about the truth or falsity of the hypothesis taking into account the results
  • What type of study would be recommended to perform to reinforce the truth of the hypothesis

As I mentioned above, it may be that the reasoning is correct, but still different experts disagree on how they have interpreted the results. It is for this reason that Stemwedel adds a couple of additional points to evaluate:

  • If the expert mentioned results, published or not, that could disprove their own conclusions
  • If the expert has taken account of these potential criticism when evaluating their conclusions

Although these last two points are valuable, it is very difficult for an expert to consider all and each one of those results that could call into question their conclusions. And here is where we return to talk about the assumptions that scientists can support their reasoning.

An assumption is nothing more than an idea that is taken for granted, and therefore does not appear explicitly in the arguments. These ideas work as well as a glue, that helps to bring coherence to the argument.

As the assumptions are ideas that are not found explicitly in the argument, Cederblom and Paulsen tell us that we need a good dose of inventiveness and creativity to bring them to the light. But, is there any guide that help us to detect the assumptions in a more systematic way? M. Neil Browne and Stuart M. Keeley, in his work, Asking the right questions, we provide a series of helpful questions to expedite the process.

According to Browne and Keeley, there are two types of assumptions: assumptions valuation (value assumptions) which are ideas that we provide standards of conduct by which we judge the behavior of others; and assumptions descriptive (descriptive assumptions), which are beliefs about the way the world is. So:

For the assumptions impairment losses, we can ask ourselves (p. 63):

  • What is the background of the author?
  • Why are the consequences of the position of the author are so important to him?
  • Can we find disputes are similar in that we identify those assumptions?
  • Can we adopt a position opposite to that of the author, to identify what values are important in that position?
  • Can we identify value conflicts common in the author’s position (for example, individual responsibility vs. collective)?

For the assumptions descriptive, we can ask ourselves (p. 79):

  • Can we identify any weakness between the reasoning and the conclusion?
  • Can we identify the ideas that support the reasons offered by the author?
  • Can we adopt a position opposite to that of the author?
  • Can you imagine other reasons that support the same conclusion?

Bibliography:

Cerderblom, Jerry; Paulsen, David. Critical reasoning. Wadsworth Publishing Company, 2005.

Neil Browne, M.; M. Keeley, Stuart. Asking the right questions. New Jersey: Pearson, 2007.

Credits:

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