##1). An agriculture company is testing a new product that is designed to make plants grow taller. This can be thought of as a hypothesis test with the following hypotheses.
Is the following an example of a type I or type II error?
The sample suggests that the product does not change the height of the plant, but it actually does make the plant grow taller.
##2). In the 2010 General Social Survey, 17% of Americans said that they had no religious preference. In other words, they said they had “no religion” rather than being Protestant, Catholic, Jewish, or another religion. In 2014, the General Social Survey sampled 2,538 Americans and found that 21% of those surveyed said that they had no religious preference. The resulting p-value is 0.0003; thus, the null hypothesis is rejected. It is concluded that there has been an increase in the proportion of Americans who have no religious preference between 2010 and 2014.
What type of error is possible in this situation?
##3). Suppose the results indicate that the null hypothesis should be rejected; thus, it is possible that a type I error has been committed.
Given the type of error made in this situation, what could researchers do to reduce the risk of this error?
##4). Suppose the results indicate that the null hypothesis should not be rejected; thus, it is possible that a type II error has been committed.
Given the type of error made in this situation, what could researchers do to reduce the risk of this error?
Type I error is rejecting the true null hypothesis
Type II error is failing to reject the false null hypothesis
1) Here the sample suggests that the product does not change the height of the plant, but it actually does make the plant grow taller.
So here we have failed to reject the false null hypothesis
Hence it is Type II error
2) Here we reject the null hypothesis
So it is possible that null hypothesis might be true but we reject it
So we would be making Type II error
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