You have probably learned that the scientific method is a series of steps that help to investigate. To answer those questions, scientists use data and evidence gathered from observations, experience, or experiments to answer their questions.But scientific inquiry rarely proceeds in the same sequence of steps outlined by the scientific method. For example, the order of the steps might change because more questions arise from the data that is collected. Still, to come to verifiable conclusions, logical, repeatable steps of the scientific method must be followed. This video of The Scientific Method Made Easy explains scientific method succinctly and well.
The most important thing a scientist can do is to ask questions.
- What makes Mount St. Helens more explosive and dangerous than the volcano on Mauna Loa, Hawaii?
- What makes the San Andreas fault different than the Wasatch Fault?
- Why does Earth have so many varied life forms but other planets in the solar system do not?
- What impacts could a warmer planet have on weather and climate systems?
Earth science can answer testable questions about the natural world. What makes a question impossible to test? Some untestable questions are whether ghosts exist or whether there is life after death. A testable question might be about how to reduce soil erosion on a farm. A farmer has heard of a planting method called “no-till farming.” Using this process eliminates the need for plowing the land. The farmer’s question is: Will no-till farming reduce the erosion of the farmland?
To answer a question, a scientist first finds out what is already known about the topic by reading books and magazines, searching the Internet, and talking to experts. This information will allow the scientist to create a good experimental design. If this question has already been answered, the research may be enough or it may lead to new questions.
The farmer researches no-till farming on the Internet, at the library, at the local farming supply store, and elsewhere. He learns about various farming methods; he learns what type of fertilizer is best to use and what the best crop spacing would be. From his research he learns that no-till farming can be a way to reduce carbon dioxide emissions into the atmosphere, which helps in the fight against global warming.
With the information collected from background research, the scientist creates a plausible explanation for the question. This is a hypothesis. The hypothesis must directly relate to the question and must be testable. Having a hypothesis guides a scientist in designing experiments and interpreting data.
The farmer’s hypothesis is this: No-till farming will decrease soil erosion on hills of similar steepness as compared to the traditional farming technique because there will be fewer disturbances to the soil.
To support or refute a hypothesis, the scientist must collect data. A great deal of logic and effort goes into designing tests to collect data so the data can answer scientific questions. Data is usually collected by experiment or observation. Sometimes improvements in technology will allow new tests to better address a hypothesis.
Observation is used to collect data when it is not possible for practical or ethical reasons to perform experiments. Written descriptions are qualitative data based on observations. This data may also be used to answer questions. Scientists use many different types of instruments to make quantitative measurements. Electron microscopes can be used to explore tiny objects or telescopes to learn about the universe. Probes make observations where it is too dangerous or too impractical for scientists to go. Data from the probes travels through cables or through space to a computer where it is manipulated by scientists.
Experiments may involve chemicals and test tubes, or they may require advanced technologies like a high-powered electron microscope or radio telescope. Atmospheric scientists may collect data by analyzing the gases present in gas samples, and geochemists may perform chemical analyses on rock samples.
A good experiment must have one factor that can be manipulated or changed. This is the independent variable. The rest of the factors must remain the same. They are the experimental controls. The outcome of the experiment, or what changes as a result of the experiment, is the dependent variable. The dependent variable “depends” on the independent variable.
The farmer conducts an experiment on two separate hills. The hills have similar steepness and receive similar amounts of sunshine. On one, the farmer uses a traditional farming technique that includes plowing. On the other, he uses a no-till technique, spacing plants farther apart and using specialized equipment for planting. The plants on both hillsides receive identical amounts of water and fertilizer. The farmer measures plant growth on both hillsides. In this experiment:
- What is the independent variable?
- What are the experimental controls?
- What is the dependent variable?
The independent variable is the farming technique—either traditional or no-till—because that is what is being manipulated. For a fair comparison of the two farming techniques, the two hills must have the same slope and the same amount of fertilizer and water. These are the experimental controls. The amount of erosion is the dependent variable. It is what the farmer is measuring.
During an experiment, scientists make many measurements. Data in the form of numbers is quantitative.
Data gathered from advanced equipment usually goes directly into a computer, or the scientist may put the data into a spreadsheet. The data then can be manipulated. Charts and tables display data and should be clearly labeled. Statistical analysis makes more effective use of data by allowing scientists to show relationships between different categories of data. Statistics can make sense of the variability in a data set. Graphs help scientists to visually understand the relationships between data. Pictures are created so that other people who are interested can see the relationships easily.
In just about every human endeavor, errors are unavoidable. In a scientific experiment, this is called experimental error. What are the sources of experimental errors? Systematic errors may be inherent in the experimental setup so that the numbers are always skewed in one direction. For example, a scale may always measure one-half ounce high. The error will disappear if the scale is re-calibrated. Random errors occur because a measurement is not made precisely. For example, a stopwatch may be stopped too soon or too late. To correct for this type of error, many measurements are taken and then averaged. If a result is inconsistent with the results from other samples and many tests have been done, it is likely that a mistake was made in that experiment and the inconsistent data point can be thrown out.
Scientists study graphs, tables, diagrams, images, descriptions, and all other available data to draw a conclusion from their experiments. Is there an answer to the question based on the results of the experiment? Was the hypothesis supported? Some experiments completely support a hypothesis and some do not. If a hypothesis is shown to be wrong, the experiment was not a failure. All experimental results contribute to knowledge. Experiments that do or do not support a hypothesis may lead to even more questions and more experiments.
After a year, the farmer finds that erosion on the traditionally farmed hill is 2.2 times greater than erosion on the no-till hill. The plants on the no-till plots are taller and the soil moisture is higher. The farmer decides to convert to no-till farming for future crops. The farmer continues researching to see what other factors may help reduce erosion.
As scientists conduct experiments and make observations to test a hypothesis, over time they collect a lot of data. If a hypothesis explains all the data and none of the data contradicts the hypothesis, the hypothesis becomes a theory. A scientific theory is supported by many observations and has no major inconsistencies. A theory must be constantly tested and revised. Once a theory has been developed, it can be used to predict behavior. A theory provides a model of reality that is simpler than the phenomenon itself. Even a theory can be overthrown if conflicting data is discovered. However, a longstanding theory that has lots of evidence to back it up is less likely to be overthrown than a newer theory.