In his 1974 Caltech Commencement Address, Richard Feynman delved into the importance of scientific integrity. At a basic level, he described the scientific method as a way of determining which ideas work and which ideas don’t. Having such a tool, one would expect to be in an Age of Enlightenment, but he laments the fact that beliefs in UFOs, astrology, mysticism, reflexology, ESP, etc. persist. Being perennially curious, Feynman investigated why people held these believe even though he thought the beliefs themselves to be laughable. (He goes on to list a few more areas where science should be applied, education and criminal justice, but he doesn’t acknowledge the potential ethical complications with doing experiments on people.)
Then, Feynman describes an example of such false beliefs from a very different perspective.
In the South Seas there is a Cargo Cult of people. During the war they saw airplanes land with lots of good materials, and they want the same thing to happen now. So they’be arranged to make things like runways, to put fires along the sides of the runways, to make a wooden hut for a man to sit in, with two wooden pieces on his head like headphones and bars of bamboo sticking out like antennas – he’s the controller – and they wait for the airplanes to land. They’re doing everything right. The form is perfect. It looks exactly the way it looked before. But it doesn’t work. No airplanes land. So I call these things Cargo Cult Science, because they follow all the apparent precepts and forms of scientific investigation, but they’re missing something essential, because the planes don’t land.
What’s missing here, according to Feynman, is scientific integrity. When reporting your results as an experimenter, you should disclose your assumptions – what could invalidate your results. You should disclose results you ruled out by experiements with negative results. You should disclose any facts that disagree with your hypothesis, even if you reached statistical significance in spite of them. In the case of combining ideas to form an elaborate theory, you must apply the theory to a new idea and make sure it comes out right instead of only including the ideas that lead you to the theory in the first place. In short, “the idea is to try to give all of the information to help others to judge the value of your contribution.”
Another example of human expectation getting in the way of science finding the hard truth right away is in the field of atomic physics. In 1909, Robert Millikan and Harvey Fletcher performed an experiment where they measured the charge of an electron by observing falling drops of oil. It was later discovered that the charge they calculated was too low because the value they used for the viscosity of air was wrong. However, follow-up experiments didn’t find the correct value right away. Instead, if you graph the values these experiments got in a line chart, you’d see them continue to rise until they finally settled on the correct value. So, experiementers were suspicious of values too high above Millikan’s and Fletcher’s calculations, so they would do more experiments if their answers were high, and if they were close to the 1909 values, they would be satisfied. This example isn’t one of scientific dishonesty, but it does show the lasting effects of disseminating dubious data.
To achieve scientific integrity, the first step is to be honest with yourself, meaning you need to be skeptical of your findings and vigilant in your pursuit of the truth. In Feynman’s own words, “The first principle is that you must not fool yourself – and you are the easiest person to fool.” Taking it a step further, Feynman believes that you should publish your results no matter how they come out, even if they don’t show much of anything at all. This is the ultimate for of honesty and integrity, by not filtering out certain types of scientific work.
I’ll conclude this summary of Mr. Feynman’s talk with an example he gives on experimenting with rats. Of course, no data scientist is working directly with rats, but I think it offers good insight into how tightly controlled an experiment must be and how critical a data scientist must be in evaluating the quality of the data.
[T]here have been many experiments running rats through all kinds of mazes, and so on – with little clear result. But in 1937 a man named Young did a very interesting one. He had a long corridor with doors all along one side where the rats came in, and doors along the other side where the food was. He wanted to see if he could train the rats to go in at the third door down from wherever he started them off. No. The rats went immediately to the door where the food had been the time before.
The question was, how did the rats know, because the corridor was so beautifully built and so uniform, that this was the same door as before? Obviously there was something about the door that was different from the other doors. So he painted the doors very carefully, arranging the textures on the faces of the doors exactly the same. Still the rats could tell. Then he thought maybe the rats were smelling the food, so he used chemicals to change the smell after each run. Still the rats could tell. Then he realized the rats might be able to tell by seeing the lights and the arrangement in the laboratory like any common sense person. So he covered the corridor, and still the rats could tell.
He finally found that they could tell by the way the floor sounded when they ran over it. And he could only fix that by putting his corridor in sand. So he covered one after another of all possible clues and finally was able to fool the rats so that they had to learn to go in the third door. If he relaxed any of his conditions, the rats could tell.
Now, from a scientific standpoint, that is an A-Number-1 experiment. That is the experiment that makes rat-running experiments sensible, because it uncovers the clues that the rat is really using – not what you think it’s using. And that is the experiment that tells exactly what conditions you have to use in order to be careful and control everything in an experiment with rat-running.
I looked into the subsequent history of this research. The next experiment, and the one after that, never referred to Mr. Young. They never used any of his criteria of putting the corridor on sand, or being very careful. They just went right on running rats in the same old way, and paid no attention to the great discoveries of Mr. Young, and his papers are not referred to, because he didn’t discover anything about the rats. In fact, he discovered all the things you have to do to discover something about rats. But not paying attention to experiments like that is a characteristic of Cargo Cult Science.