Understanding Science: Big Ideas and Language

Science is a democratic process that moves forward by building consensus.

But what is it, really? According to dictionary.com, science is the “systematic knowledge of the physical or material world gained through observation and experimentation.” That’s right. Science is a human endeavor. It is the way we objectively try to make sense of our world and build a framework that allows us to understand how things work. It is important to realize that science is an ongoing work in progress. However it is just as important to realize that science builds upon prior results of science. Once a consensus has been reached by a majority of scientists who are respected in that field, usually that problem isn’t considered an interesting avenue of research anymore. This is an important point because sometimes when the prevailing scientific results of the day are unpopular politically or with certain advocacy groups, persuasive individuals with great oratorical skills can convince the general populace that controversy exists when in fact there is none (Ceccarelli, 2008).

Scientists try to collect data that will distinguish between competing ideas.

They are naturally skeptical and looking for ways to disprove a hypothesis. Here are some important points. A hypothesis that can’t be tested is not useful. Data that support multiple hypotheses is not very useful. Collecting more of the same data that supports your favorite hypothesis is not good enough. Good science involves an active search for observations and data that will disprove a hypothesis.

Scientists make observations and collect data in a careful, organized, and systematic way to test their ideas.

This is where the work comes in. Scientists spend a lot of time rechecking and duplicating their observations, carefully writing up their results, and then publishing them.

Scientific work that is peer-reviewed and published is more likely to be accepted by other scientists.

Peer review is a way that science is validated inside the scientific community. Most journals as well as funding agencies enlist the voluntary help of other scientists in the field to judge the merit of the science being submitted for publication. The journals themselves choose the reviewers. If you are an active scientist who publishes then part of the expectation is that you will spend some of your free time as a peer reviewer. Just because a paper has been peer reviewed does not mean it is correct beyond all doubt (Koerth-Baker, 2011). In fact, peer review is not a good way of catching deliberate fraud. But it does provide a check on sloppy work, or work that has failed to test a hypothesis in a useful way. (Koerth-Baker, 2011) For me, if I see that a paper has been published in a standard journal then I know it has gone through peer review. If someone claims that a self-published report has been peer-reviewed then I hear an alarm bell because I wonder what that really means. To reiterate, peer review does not guarantee infallibility but the absence of peer review means you should check the author's’s background to find out why the report wasn’t published in a standard peer-reviewed journal.

Helen Quinn, former president of the American Physical Society, wrote a nice piece in Physics Today in 2008 about how the language scientists use can be misinterpreted by the general public. The table below is a summary of many of her points:

word/phrase what the public means what a scientist means
believe Usually it's a statement of faith or else a statement of uncertainty, as in "I believe it will rain today, but I'm not sure." “Scientists believe . . .” means that all the evidence collected so far supports this viewpoint and furthermore opposing viewpoints have no evidence to support them. It’s basically analogous to a regular person stating a fact they know.
theory/hypothesis guess, hunch a complex construct of well-tested observations that describes some part of how the world works as well as predicts it.
error something that is wrong margin of variability given the limits as to how precise you could possibly be
uncertainty not sure, don’t know the limit of the state of the art knowledge given a complex system or not enough data