Howdy! Today we'll be discussing how science and scientific evidence are used in national debates. Briefly, we will discuss what the scientific method is and what it is not. We will also explore some common ways of manipulating scientific data to fit narratives that are often in the media or used by politicians. One common misconception is that science can prove things.
However, proofs are something only found in logic and mathematics. Science can only offer evidence to support theories. Now I'm sure that all of you have seen a figure similar to this one that walks you through the scientific method, but just to be sure that we're all on the same page, let's quickly discuss a brief example of how this works.
So let's say that a scientist observes that individuals who drink from a specific water pump are becoming sick with cholera. Then the scientists think of specific questions to ask. The example given is that is there something in the water that is making people sick?
They hypothesize that germs and not bad smells are causing the illnesses. They then predict that if they take away the access to the water, then fewer people will become sick. Scientists test this by removing the handle of the water pump.
After analyzing the data and finding a sharp reduction in the cases of disease, the scientists in this example then conclude that germs in the water were causing the outbreak. Finally, they communicate these findings to the community. In practice, this process takes a lot of times and needs replication and lots of hypothesis testing.
Even after all of this, we only have strong evidence for and not proof of the germ theory of disease. When can theories become facts? Well, never is the answer. Scientists can observe facts and create hypotheses to test, and then analyze the results of those tests to come to conclusions. After numerous experimental trials reproduce the same or similar results, we can create a theory.
While this is a very simplified model, it just shows you that it takes extensive testing for a hypothesis to become a theory. Theories can help explain factual observations and represent the best possible scientific explanation for a specific phenomenon. However, theories are never facts. Scientific findings are most often expressed with numbers and statistics, and this is often presented to the public through the media. While statistics provide a powerful tool for understanding correlations between variables, such as carbon emissions and climate change, or water fluoridation, and tooth decay prevention.
However, statistics can be manipulated or presented in a way that is less than honest. Throughout this module, we'll be looking at just a few examples of how media and politicians can take scientific findings and manipulate them to fit their own narrative. Oftentimes, there is confusion between causation and correlation.
For instance, shown in the graph is pounds of mozzarella cheese consumed in the United States as well as the number of degrees awarded in civil engineering from 2000 to 2009. This graph shows a very strong correlation of 96%, meaning there is a 96% agreement between these two variables. So, does cheese consumption cause individuals to pursue a degree in civil engineering? Probably not. While correlation can often be a sign that two or more variables are related, this is not always the case.
Correlation does not necessarily mean causation. Although correlated items may indicate there is a relation, more research would be required in order to prove causation. Within the media, correlation and causation are often confused. So for instance, journalists often confuse causation and correlation. As you can see, correlation does not necessarily mean causation.
When it comes to representing findings, there are three types of lies. Lies, damn lies, and statistics. That's a quote by Mike Twain, and it's a quote that really resonates with problems that we see in modern media and political narratives today, where it's very easy to take scientific evidence and misrepresent or skew how the data is visualized. So if you consider these two graphs, let's imagine that a company designed a new filtration system to cut carbon emissions at industrial sites.
This filter would be placed upon stacks and monitored throughout the year. After one year with the filter in place, the company compared the carbon emissions in pounds per day without the filter represented by the green bar and again at the end of the year represented by the blue bar with the filter. If you wanted to show that the new filters greatly reduced carbon emissions in the best possible light, which graph would you use?
You may not realize it, but these graphs are actually the same graph. The one on the left simply zooms way in to show a very small change that makes it look really big, while the one on the right shows the entire scale. This is a common way that scientific evidence can be misrepresented. So as we have seen, science and scientific evidence have a tendency to be misrepresented or grossly oversimplified by the media and politicians.
I'm sure all of you have encountered a variety of memes that really have a tendency to vastly oversimplify or misrepresent the information. Let's look at a few real world examples of how this happens. For instance, is ice melting across the globe?
Well, this article discusses a NASA research project showing that Antarctica is gaining and not losing ice, as a gain of approximately 82 billion tons per year. At best, this article seems to reveal a contradiction in climate science. At worst, it indicates that climate science is wrong. So, what's the problem with this reporting? Well, in fact, it never actually mentions the other major ice sheet over Greenland, which is losing approximately 270 billion tons of ice per year.
If we look at the whole picture, you'll realize that despite the gains in Antarctica, There is a global net loss of 188 billion tons per year. This figure is higher when you take into account glaciers. Unfortunately, this type of reporting is really common, where reporters are lying by omitting important facts. Below are a few journal articles that you can look at if you're interested in getting a more in-depth picture. So what can you do?
Well, It's important to be very knowledgeable of basic science and statistics. Hopefully by the end of this class you'll have a strong set of tools and a broad-based set of understanding of basic environmental science and you'll have the tools necessary in order to pick apart potential misleading scientific evidence. When you're reading a news article on a specific topic, please try and find and read the study being discussed.
Oftentimes Articles have a tendency to just report what other reporters have covered. And if you are able to trace back to the original news report, journalists will often include a link to original studies. In this way, you will be able to track down the original study either through Google Scholar or through the TAMU libraries. Bias knows no political affiliation, so bias can be found on both sides of the aisle. The importance of this class is to help students gain a set of tools to be able to think critically about what they're reading in the news or what they're reading or hearing from politicians.
The topics in this course will include deep discussions on power and authority and how bias and prejudice can sometimes skew or misrepresent scientific evidence. Next step is to know where you're getting your news from. Is it credible?
If you're interested in learning a little bit more about types of bias that shape your worldview, I've attached a TED Talk that's really great and also a podcast on politics in the environment. Again, just to reiterate, you do not have to listen to all of these podcasts. Just pick one that you are interested in. I've provided a variety of options, mainly to give you the opportunity to find what you're most interested in. And if you're interested in learning more, you can always do a deeper dive.