Curiosity is one of the most fundamental human traits. We are all curious about something. Is there life on Mars? Why is my hair so curly? What causes diseases, and how can we cure them? How do we feed all the world's people? We may have gut feelings or assumptions about the answers to these questions, but true problem solving goes further than that. Scientists combine their curiosity about the world with data and observations, to find answers to the questions that interest them. There is no fixed path that leads directly to scientific knowledge, but there are certain features of science that give it a distinctive mode of inquiry. Science is based on observable, measurable evidence. To get at the evidence, scientists use what is called the Scientific Method, which is a series of steps that help them think and work in a rational, systematic, and error-minimizing way. The steps usually followed are: one, define a problem, two, form a hypothesis, three, carry out experiments or observations, four, analyze data, and five, form a conclusion and communicate results. These steps have been used for many hundreds of years by researchers and scientists. They also can be used for almost any type of problem or question, such as improving your batting average or finding the most efficient way to get to your next class. Anything that involves trial and error testing and observation. But the first step is to identify and define the problem. Problems for scientists to explore come from a variety of places. A scientist may work on a well-known problem which affects all people, such as a cure for cancer, AIDS, or heart disease. Or they may attack such worldwide problems as pollution or hunger. This type of research is called Applied Science. Other scientists work on problems out of curiosity or a special interest that they have. Like, what makes a lightning bug glow, or what materials make up other planets. This is called Pure Science. Its purpose is to answer questions and, unlike Applied Science, it has no immediate application to our daily lives. Everyone can see the need of Applied Science. It attacks real and pressing problems. However, many people do not understand Pure Science, and consider it a waste of time and money. But all science adds to our knowledge of the world we inhabit, and information gained through Pure Science research has been shown later to have great value to us. Pure Science is the foundation upon one which most of Applied Science is based. A classical example is penicillin. Alexander Fleming was growing bacterial cultures on plates. And he left them. He went away for a vacation. When he came back, his plates were all contaminated with this mold. And instead of throwing them away, he looked at it and he said, you know, this mold is killing these bacteria. I wonder what's doing that. He had that curiosity. What's killing those bacteria? Did some further experiments, purified the specific protein from that mold that was killing the bacteria. And that was penicillin. In either case, the first step in the scientific method is to accurately and specifically define a problem. Questions may range from the mundane. How does a straw work? To the fantastic. Are there other planets with life like ours? But the important thing is to focus the question so that the problem can be investigated through experimentation and observation.
A hypothesis is a possible answer to a question or problem that a scientist wishes to solve. A hypothesis is a guess, because the outcome of an experiment is never known before it starts. But it is called an educated guess, because it is based on the best information which can be gathered at the time. Before a scientist forms a hypothesis, a great deal of research is done to review what is already known about the topic. This knowledge can show the researcher what has not worked in the past, and it often guides the formation of a new hypothesis. A scientific hypothesis must meet an essential requirement. It must be testable. Hypotheses are often stated in an if...then format. The hypothesis is proven right or wrong by the experiment. Here are some examples. Students who get eight hours of sleep the night before a math test will score higher than students who get only six hours of sleep. Or, if someone who suffers from arthritis takes 500 milligrams of aspirin, then they will report experiencing less pain. If you're changing shampoo because you don't like the way your hair looks, you say, is it my shampoo? You go out, you buy a new shampoo, that's your hypothesis. You test it on your hair, and you see whether you like your hair better. We also go to a lot of trade shows that will have people introducing new products. The chemical companies will come out with a new material, and they'll be targeting it at the automotive group, or the automotive area, and we'll say, maybe that's something we can apply in sporting goods. So that's what we'll get a hypothesis from. Let's take this. And I think, if we use this material in a golf ball cover, maybe we can get it to spin a little bit better, with a little bit better durability. Many people confuse the word theory with hypothesis. While hypotheses are formed before the research is started, theories are produced after many experiments and explain a body of data. A theory is a strong statement of confidence for scientists. But even a theory can change. Theories help produce new hypotheses, which generate new data that confirms or challenges the theory. This evolving cycle leads again to new hypotheses and thus, new experiments. I remember one of the quotes that I had from college, and the professor came out on the first day, and he said, good morning. He said, half of what I'm going to tell you in this class is a lie. And everyone looks around and says, gee, I'm paying a lot of money for this class, what's going on? He said, I'm going to repeat that. Half of what I'm going to tell you in this class is going to be a lie. He said, I'm not intending it to be a lie, but it's the knowledge I have right now. In the future, half of what I am telling you is going to be proven to be wrong. Because it's theories and hypotheses that people are promulgating at this point, and they're going to be found out to be not true. But that's how you advance the method, you advance science, by saying here's my idea, you prove me wrong. Otherwise, I'm going to continue with this idea, because it is, I think, right. Almost any conclusion that's been made in science is not 100% accurate. Almost every scientist has been wrong more than they've been right. But again, science is all based on a foundation. And say that conclusion is half right, we can use that conclusion as a foundation for further experimentations. Experiments are carefully designed activities which test the hypothesis. The hypothesis has been formed based on observations and ideas about how the world operates. Now it's time for the confrontation
between the hypothesis and the way the world actually works. This is the experiment. For data to be accepted by scientists, it must be obtained in such a way that the researcher can be sure of it. Not all information we observe can be used to draw good conclusions. For instance, you may have heard someone say, I took Vitamin C tablets last winter and I did not get a single cold. That shows that Vitamin C prevents the common cold. That evidence alone is not enough to make such a statement, because the person can't be sure that they would have had the cold if they had not taken the Vitamin C. And the data would only apply to the one person, not to all people. In order for the information obtained in the experiment to be scientifically valid, many things must be carefully regulated in the experiment. One, we must be sure that the thing being tested caused the change we observe. Two, we must be able to apply the answer we find to many situations outside the test. Three, we must be sure that the results obtained are real, and not the result of A, another factor which affected the experiment, or B, bias on the part of the experimenter or anyone involved in the experiment. To be sure of the cause and effect, most experiments use an independent variable, a dependent variable, an experimental group, and a control group. The independent variable is the element of the experiment that is being manipulated, and the dependent variable is the thing being measured. The experiment is actually performed on the experimental group. The control group serves as a standard of comparison, using the same conditions as the experimental group except the one factor or variable being tested. For example, if we wish to test Vitamin C's effect on colds, we would select two groups of similar people, our experimental group and our control group. The experimental group would receive Vitamin C, which is our independent variable. The control group would not receive Vitamin C. The presence or absence of a cold is the dependent variable in this case. That's what we're measuring. We want to be sure that our answer applies to all people, so we must include both males and females of all ages in our study. We would do what scientists call repeated trials, multiple tests of the same experiment, to be very sure of the outcome. We would also have to consider other variables that could affect the outcome of the experiment. To be sure that our results are really caused by Vitamin C, we would have to think of all the other things which could affect the outcome of the experiment. These are control variables. For instance, the general health of the subjects would influence how easily they would catch a cold. So would their diet, amount of exercise, contact with other people with colds, age, sex, and other medications they might take. Our task would be to make sure that the people in the experimental and control groups are as similar as possible, when it comes to these variables. This is not an easy job when humans are used as test subjects. Therefore, the use of repeated trials helps average out the differences and makes our confidence in the data higher. Another problem when doing experiments is the human brain. All of us have certain beliefs or biases which can affect the outcome of experiments. For example, there's a well known reaction known as the placebo effect. Most people think that a pill will make them feel better o cure an illness. If the experimental group is given a pill, human nature may cause them to feel better simply because they have taken a pill. On the other hand, the control group may feel worse than normal simply because they did not. To combat this reaction, researchers may do what is known as a single blind experiment.
Here's how it would work in our Vitamin C example. A pill that looks and tastes identical is given to each group. The experimental group receives the pill containing Vitamin C and the control group takes a pill containing only starch or sugar. Only the researchers would know which group received the Vitamin C. Even when the placebo effect is controlled using a single blind experiment, sometimes human bias still affects the outcome. Scientists, though highly trained, are still people, and can have their own biases. After spending many months on research, they may be more likely to make observations which support their own hypothesis. For example, they may be more likely to see improvement in the Vitamin C group if they already believe that Vitamin C helps colds. People are human, and they have their natural tendencies to be biased toward their idea. People always want their ideas to be correct. And so they'll analyze their data sometimes with that tinge of, well, this is what I expected to happen, so I can try to fit the data into that conclusion. The double blind experiment is used to eliminate this bias. With this method, someone not involved in the research gives the pills to each group. Both the subjects of the research and the scientists are blind to which pill was given to whom. The scientists only find out after the experiment is over and the data has been gathered. Many experiments don't need single blind or double blind techniques. These are most helpful when the subjects of the experiments are humans, in order to eliminate bias from the data. When the experiments are complete, it's time to analyze the data to determine if anything has been learned. Scientific problem solving is based on the interpretation of data, or evidence gathered from observation and experimentation. Scientists study the results of the experiments, looking for a significant difference between the control and experimental groups. They want to be sure that there is a real effect taking place, not just a random variation due to chance or errors in measurement. For example, in our Vitamin C experiment, if 15 of 100 people who took Vitamin C got colds and 17 of 100 people who took no Vitamin C also got colds, most scientists would not conclude that the Vitamin C had a large effect on colds. This would be a case in which the data does not show a significant difference between the Vitamin C group and the control. However, if only 10 people receiving Vitamin C got colds, while 50 people got colds in the control group, then it would probably be concluded that the Vitamin C had a significant effect. Scientists use statistics to help them decide if the effect in the experiment is large enough to be called significant. The commonly accepted standard for significant difference is the confidence level of at least 95 percent that the independent variable caused the effect on the dependent variable. When scientists form conclusions, they then report the findings in print for other scientists to read, respond, and criticize. Science never accepts the first answer to a problem as the absolute answer. The experiment must be repeated by the original scientific team, as well as other scientists in the same field. If the results of these repeated trials are the same as the original experiment, then scientists are more confident in thinking the answer is correct. Understand that no answer obtained in an experiment is 100 percent accurate. This is why, when reporting results, instead of saying, Vitamin C prevents colds, scientists would say something like, the
data seems to indicate that Vitamin C may be effective in preventing colds in some people. Does this mean we should not pay attention to scientific results, because they are not 100 percent true? No. Though science is not absolute and always open to new hypotheses, scientific findings are the results of a thorough process of observation and testing, and they do provide us with valuable data. For instance, a seat belt might trap you in a car which plunges into a river. But data suggests that seat belts work effectively more than 99 percent of the time and save many lives each year. So you should play the odds and wear your seat belt. The scientific method is used every day to develop products that affect our lives in major ways. Let's say you're a company like Spaulding, which makes products like Top-Flite golf balls. Every day, golfers across the world try to improve their game using Top-Flite balls. How could Spaulding use the scientific method to help? Our aim here is to make sports easier for the average guy to play. And we try and use the multidisciplinary areas that we have to apply the different materials to that end. Someone will come in, with an idea that they've garnered from either the automotive industry, the computer industry, the gaming industry, meaning toys and board games. We look at areas that you wouldn't think would be applied to sporting goods. An example would be tires. There's been a big initiative on rolling resistance on tires, to try and get tires to roll better. So you get better gas mileage. So you'd say, that's neat. What does that have to do with sporting goods? Well, tires are rubber, and they tend to have good resilience. So we like to look at what happens when we take that resilience. What happens when we take that chemical compound that's being used in that tire, and apply it now to a golf ball core. Then we'll go test it. And typically, sporting goods are dynamic. So we'll hit it, we'll drop it, we'll punch it, we'll bat it. We'll hit it with a golf club, and see what the response is. We have on staff a consultant for mathematical work. We have a physicist on staff. We have an aerodynamicist. We have several chemistry people on staff. So when the guys out there hitting the softball, or hitting the golf ball, I don't know if he knows what's behind it, but there's a whole team of people that are up late at night, a lot of times, thinking about how to make this thing easier for people to work with. One of the most important applications of the scientific method is in the development of drugs. About two billion prescriptions are filled every year in the United States. It is so common to use drugs to cure illnesses that we take them for granted. And yet, by the time a new drug is approved for use by the public, it has been through a long and costly researching and testing process. An average of eight and a half years and $359 million is needed to get a drug approved for human use in the United States. Our experiment is, can we find a chemical that will affect the function of that protein. If we can do that, we'll be affecting that function of that protein to actually help cure the disease. And so to do that, we have to make the protein, that's part of our experiment. And we end up putting it in a little tube, like this, and then distribute all that protein into a plate like this. And in that plate we put all our various chemicals. And we try to see whether a chemical will interact with that protein or enzyme, change its activity or its function, and will that actually affect the disease in a positive way. Can we then cure the disease with that chemical?
That chemical, after subsequent tests in animals and in people, we hope to become a medicine that we can then sell to people, to basically help with their diseases. That process is often ten years. So you can imagine the amount of data that has to go in from the beginning to the end of this project. And the federal government basically wants to see all of it. And so we present our data to them. Then there's a hearing. They ask questions about the safety of the medicine. That's clearly an important part of it, and we have separate clinical trials just to analyze the safety of it. And then, the efficacy. Did it work as well as it should have? Does it work better than medicines that are already on the market? If it doesn't work any better, and it's very costly, well we're just not going to be able to sell that medicine. It doesn't make sense for people to take it. These are just a few examples of how the scientific method is used in almost everything we see and touch. After all, think about how a glue company might test advances of its product, or a rubber manufacturer, or a car maker. Most companies use the scientific method in some way to develop products and services that better people's lives every day. It's true that you can also use the scientific method in your everyday life. Let's say you play on a baseball team and you're not hitting the ball as well as you, or your teammates, would like. First, define the problem, and then limit it to one thing, to make the test simple. Let's say that you wish to hit the ball farther. Your hypothesis would be based on what you know about hitting, and perhaps advice from your teammates and coach. After some thought, you decide your hypothesis is, if I change to a new, heavier bat, then I will hit the ball farther. Now it's time to conduct an experiment to test the hypothesis. Variables, such as your cleats, batting gloves, the way you stand in the batter's box, how you hold the bat and swing, and the way the ball is pitched would need to be as identical as possible throughout the experiment. With the last variable, you would want the same person to pitch to you the same way every time with each bat. To start, you might want to alternate hitting five pitches with your old bat, followed by five pitches with your new bat, to avoid getting tired with one bat before swinging with the other. Then you would measure the distance the balls traveled, and calculate an average distance that you hit the balls with each bat. Repeat the experiment several times, and even on different days, to make sure of your data. When you analyze your results, you would have to decide if the difference in the distance you hit the ball justifies changing your bat for the new one during games. Even if the experiment showed little or no difference, you may discover during the experiment that some other factor may be causing your hitting problem, and decide to conduct a different experiment to test it. This is how science works. The cycle of questioning and testing questioning and testing, leads to a more precise way of thinking and a better understanding of the way the world works.