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If you recall from the previous lesson, the experimental method consists of conducting experiments to understand the cause behind something and how one variable might affect another one. The variables in question are the explanatory variable and the response variable.
When you hypothesize that a specific change in variable a causes a change in variable b, you base your educated guess on what you have observed. The experimental method allows you to set the parameters for the experiment and then go ahead and determine if there’s any cause to support your hypothesis.
IN CONTEXT
To see how a hypothesis might work for making a prediction about the cause and effect that exists between a couple variables, let’s assume that you have a particular person, Lars, who goes to the doctor because his blood pressure has been a little high. The doctor talks to Lars about how to manage that.
Lars starts exercising more regularly, and at a follow-up visit a month later, his blood pressure is relatively low. Lars hypothesizes that, since his blood pressure went down, the exercise led to that change. His hypothesis is an educated guess about the relationship between these two variables.
Your hypothesis states what you believe to be the case, but other explanations may exist. Perhaps rising insurance rates or decreased incomes have caused people to buy smaller vehicles.
Take a look at the relationship that exists between education and the income someone earns throughout the course of his or her career. If you have a college degree, it typically means your earnings will be higher over the course of a lifetime, but is getting the degree causing higher incomes, or could there possibly be something else going on here?
Your hypothesis might be simple: A college degree translates to higher earnings. Data would tend to back that up if you use the experimental method.
There may be several explanations, but your hypothesis states what you believe to be the case. It is an educated guess based upon what the data is telling you.
There are two types of hypotheses. One is a null hypothesis, which states that there’s no relationship between the variables in question. The other, called the alternative hypothesis, states that there is a relationship between the explanatory variable and the response variable, and defines that relationship.
A null hypothesis essentially says that it’s possible the explanatory variable is really just affecting the response variable by chance. In other words, there’s no direct relationship. It just happens to be something that’s occurring. It’s not necessarily something that’s causing the effect in the response variable.
The alternative hypothesis suggests that there actually is some cause-and-effect relationship going on between the explanatory variable and the response variable.
IN CONTEXT
Suppose you want to look at test scores and evaluate how well a given class might do on a particular exam. In particular, perhaps you are testing to see if there’s a relationship between the amount of sleep a student gets and how well he or she performs on an exam.
Your null hypothesis would be that sleep and student performance are not related. The alternative hypothesis, on the other hand, would be that perhaps increased amounts of sleep would lead to better student performance. You’d see if the relationship between sleep and student performance actually exists, and whether or not there’s a cause-and-effect relationship that exists between the two.
So, let’s go through a couple of different examples of how we would actually look at a situation and identify what a null hypothesis is under those circumstances as well as what the alternative hypothesis would be.
When you’re looking at the difference between null and alternative hypotheses, the words often used to describe a null hypothesis are equal, less than or equal to, or greater than or equal to. In the alternative hypothesis, on the other hand, you see words such as unequal, less than, greater than, or different.
Null Hypothesis | Alternative Hypothesis |
---|---|
Equal | Unequal |
Less than or equal to | Greater than (larger) |
Greater than or equal to | Less than (smaller) |
Same | Different |
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