SCIN 137 AMU week 3 lesson lab HYPOTHESIS FORMULATION AND TESTING Introduction to Meteorology American Military university
Topics to be covered include:
- Definition and the importance of hypothesis testing
- Characteristics of an effective hypothesis
- Misconceptions about hypotheses
- Relationship between hypotheses, theories, and laws
In the last lesson, we discussed how in the scientific method observations lead to well-phrased, thoroughly researched scientific questions. Now we will turn our attention to hypothesis formulation. Proposing a hypothesis involves more than simply stating your opinion or wildly guessing at the answer to a question. We will discuss characteristics of an effective hypothesis, debunk some common misconceptions, and examine the relationship between hypotheses, theories, and laws. Finally, we will examine case studies from peer-reviewed manuscripts to practice determining a researcher’s question and hypothesis.
- Greenhouse GasesOver the past few years, you have most likely encountered news stories, articles shared on social media, or even discussions in your classes about global climate change, sometimes referred to as “global warming.” Or perhaps you have noticed strange weather patterns such as milder winters or more severe thunderstorms where you live. Global climate change occurs when excessive levels of greenhouse gases, such as carbon dioxide, trap solar radiation near the Earth’s surface instead of allowing it to leave the atmosphere. Furthermore, this current warming trend is most likely due to human activities, especially our extensive use of fossil fuels that release carbon dioxide and other greenhouse gases into the air when they burn (National Aeronautics and Space Administration (NASA), 2017).
What is a Hypothesis?
A hypothesisis typically defined as an educated guess or expected answer to a scientific question based on prior knowledge and observation (Bradford, 2015c). However, we can expand this definition. Scientists propose hypotheses for unexplained natural phenomena that do not currently fit into scientific theory and do not have a pre-determine outcome. Hypotheses also include an explanation for why the researchers’ guess may be correct, usually based on background research, the results of previous studies, or observations (Bradford, 2015c).
Hypotheses play a major role in the interplay between inductive and deductive reasoning. Deductive reasoning, or deduction, begins with a broad, general statement and then narrows it down into specific hypotheses to test (Bradford, 2015a; Trochim, 2006). In general, the scientific method relies on deduction to test specific hypotheses (Bradford, 2015a). Inductive reasoning, on the other hand, uses specific observations to make broad generalizations (Bradford, 2015a). Induction often feels more open-ended and exploratory, while deductive reasoning focuses on testing specific hypotheses (Trochim, 2006). However, the two processes work closely together. For example, we can combine the experimental results from testing multiple related hypotheses to develop broader theories (Trochim, 2006), which we will discuss later in this lesson. Likewise, deduction allows us to apply broad theories to specific situations (Bradford, 2015a).
Finally, hypotheses may influence a third type of logic, called abductive reasoning. This form of reason does not fit within induction or deduction but rather uses an incomplete series of observations to develop the likeliest possible explanation (Bradford, 2015a). Abductive reasoning can help researchers develop new hypotheses to be tested based on observing a phenomenon without a clear explanation. An example of abductive reasoning includes doctors who make diagnoses based on test results and jurors who hand out verdicts based on evidence (Bradford, 2015a).
A Hypothesis Should be Testable
While a hypothesis may be an educated guess, the researcher must take care to avoid inserting personal opinions, biases, or beliefs into the hypothesis. Instead, a hypothesis should lend itself to an objective experiment that relies on quantitative observations and data. Conducting background research and stating an effective question greatly aid in proposing a testable hypothesis. For example, if you wanted to conduct an experiment on the effect of sunlight on plant growth, you would not state the hypothesis as, “I believe sunlight helps plants grow.” The phrase “I believe” implies that you are inserting your own opinions, possibly without the backing of additional research. Therefore, we turn our attention to the sentence structure and format of an effective hypothesis.
A Hypothesis Should Connect the Independent and Dependent Variables
Hypotheses should suggest a relationship between two variables that a researcher will test in an experiment. Typically, the researcher manipulates the independent variable to determine its effects on the dependent variable. We often find it helpful to format hypotheses as ‘if-then’ statements to reinforce this proposed relationship between two variables. Although the ‘if-then’ statement is a helpful framework, it is important to realize hypotheses can be phrased in other formats.
Let’s return to our scenario of testing the effects of sunlight on plant growth. Sunlight and plant growth are the two variables we are interested in testing, and we can easily manipulate light levels in a laboratory or greenhouse setting. Therefore, sunlight would serve as the independent variable. We would phrase the hypothesis to refer to sunlight in the “if” clause and plant growth in the “then” clause. Based on this information, we could devise the following hypothesis:
“If sunlight is increased, then plant growth will increase.”
We could also propose that decreasing sunlight should decrease plant growth or even that sunlight adversely affects plant growth. Our hypothesis would depend on the plant species in question and background research. For example, if we were testing the effects of sunlight on a shade-tolerant plant, then increasing sunlight to higher and higher levels may actually decrease plant growth at some point.
This hypothesis also identifies sunlight as our independent variable and growth rate as the dependent variable. You may also encounter manuscripts in journals where the authors do not state their hypothesis in this exact sentence structure. Instead, they may write, “We proposed that increasing sunlight levels should increase plant growth.” However, we could reword the hypothesis as an ‘if-then’ statement easily enough. We will practice analyzing a research manuscript later in this lesson to determine the question and hypothesis.
A Hypothesis Should Include a Null and Alternative Hypothesis
Whenever you propose a hypothesis, you actually test a null hypothesis and alternative hypothesis. The null hypothesis, commonly denoted as H0 in manuscripts, states that no statistical significance exists between the independent and dependent variable (Taylor, 2017). In other words, any patterns that you might observe are due to random chance. Researchers attempt to reject the null hypothesis so they can support their alternative hypothesis, which states that a statistical relationship exists between the two variables (Taylor, 2017). Researchers devise an alternative hypothesis that they think explains a phenomenon and then work to reject the null hypothesis (Taylor, 2017).
We can apply this concept of a null hypothesis to our plant growth scenario. A null hypothesis assumes no relationship between sunlight and plant growth, which we would attempt to reject to support our alternative hypothesis, H1:
H0: “If sunlight levels increase, then plant growth rate will not change.”
H1: “If sunlight levels increase, then plant growth rate will also increase.”
A Hypothesis Cannot be Proven, Only Supported or Rejected
Researchers must take great care to avoid saying that they have “proven” or “disproven” a hypothesis. Such universal statements rarely occur in the natural world, with rare exceptions known as scientific laws. In general, the scientific method cannot prove or disprove a hypothesis absolutely. Data gathered in scientific research can only support or fail to support a hypothesis.
Let’s consider our experiment examining the relationship between sunlight and plant growth rate. We could conduct the same experiment multiple times and support our hypothesis every time that plant growth increases with sunlight. However, we cannot repeat the experiment enough to say that we have “proven” our hypothesis. We could just as easily conduct the same experiment again and acquire different results that would force us to question our hypothesis. As we will learn in the next lesson, researchers must take care to account for as many variables as possible, barring the independent variable, that may influence the results of an experiment.
Errors May Occur When Testing a Hypothesis
Even if a scientist takes every possible precaution when researching and formulating a hypothesis, he or she may still reach the wrong conclusion after analyzing the data. A Type I error occurs when the researcher incorrectly rejects the null hypothesis, while a Type II error occurs when the researcher fails to reject a null hypothesis when actually correct (Bradford, 2015a). Scientists can reduce the risk of Type I and Type II errors through careful experimental design, control of any variables that may influence the results, repetition of the procedure, and adequate sample size.