You read only interesting findings because only those get published, get written about and popularized in social media. Experiments that find no statistically significant difference don’t leave the filing cabinets of researchers because no one wants to read a story where nothing happens. This is such an experiment, where there was not enough evidence to reject the null hypothesis.
Let us start at the beginning. This experiment is about people’s perception of a person’s competence based on whether the person speaks in absolutes with no room for alternatives or whether the person speaks in terms of likelihood, accounting for alternative explanations.
There are several examples of those who speak in absolutes with no self-doubt. Read any CEO interview (enterprise or startup), management guru’s book or Seth Godin’s blog. Examples are,
“Revenue grew because of our marketing”
“Sales fell because of Europe”
“Groupon works, it really works”
An example of speaking in terms of likelihood comes from Nobel laureates in economics,
“Answers to questions like that require careful thinking and a lot of data analysis. The answers are not likely to be simple.”
Hypotheses: You do start with hypotheses before any data analysis don’t you?
Here are the hypotheses I had about speaking in absolutes/likelihoods and perception of competence.
H1: Business leaders are judged to be more competent when they speak in absolutes. Conversely, using terms like “likely” may be perceived as wishy-washy and hence signal incompetence.
H2: Scientists are judged to be more competent when they use likelihoods and avoid absolutes. (Because scientists are expected to think about all aspects and anyone who zones in on one factor must not know how to think about acenarios)
Of course the null hypothesis is there is no statistically significant difference in perception of competence based on whether the subject in question speaks in absolutes or in likelihoods.
Experiment Design: So I designed a simple 2X2 experiment, using SurveyGizmo. You can see the four groups, Company Executive and Scientist as one dimension, Absolutes and Likelihoods on the other. I designed a set of 4 statements with these combinations. When people clicked on the survey they were randomly shown one of the four options.
Here is one of the four statements
This was a very generic statement meant to speak about results and what could have caused it. I avoided specific statements because people’s domain knowledge and preconceived notions come into play. For example, if I had used a statement about lean startup or social media it would have resulted in significant bias in people’s answers.
Based on just one statement, without context, people were asked to rate the competence of the person. Some saw this about Scientists, some about a Company Executive.
Note that an alternate design is to show both Absolute and Likelihood statement and ask the respondents to pick the one they believe to be more competent. I believe that would lead to experimental bias as people may start to interpret the difference between two statements.
Results: I collected 130 responses, almost evenly split between four groups and did t-test on the mean rating between the groups (Scientists: Absolute/Likelihood, Executive: Absolute/Likelihood, Absolute: Executive/Scientist, Likelihood: Executive/Scientist). And you likely guessed the results from my opening statements.
There is not enough evidence to reject the null hypothesis in all the different tests. That means and difference we see in competence perception of those speaking in absolutes and likelihoods is just random.
What does this mean to you?
Speaking in absolutes, a desired trait that leaders cultivate to be seen as competent and decisive leader, has no positive effect. Including uncertainties does not hurt either.
So go right ahead and present simplistic one size fits all solutions without self-doubt. After all stopping to think about alternatives and uncertainties only takes time and hurts ones brain with no positive effect on the audience.
Caveats: While competence is not an issue I believe trust perception could be different. That requires another experiment.