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Chapter 8: Problem 54

Is it acceptable practice to look at your research results, note the direction of the difference, and then make the alternative hypothesis one-sided in order to achieve a significant difference? Explain.

Short Answer

Expert verified
no, it is not acceptable practice to look at the preliminary research results, note the direction of the difference, and then make the alternative hypothesis one-sided in order to achieve a significant difference. This is because it introduces post-hoc reasoning and bias, which can lead to wrong conclusions and damage trust in scientific research.

Step by step solution

01

Understand the context

First, you should be aware that the context is that of analytical research. In these circumstances, a hypothesis is essentially a prediction that is made prior to the data collection process. There are usually two types, a null hypothesis (no effect, no difference) and an alternative hypothesis (there is an effect or difference). If these hypotheses are not established before the study or research, there is a risk of introducing bias into the results.
02

Define the terms

A one-sided or directional hypothesis predicts the direction of the expected findings. Adapting the hypothesis to match the data analysis is known as 'HARKing' (Hypothesizing After the Results are Known). On the other hand, significant difference is a statistical term that tells us how likely the observed difference would occur by chance.
03

Analyze the acceptability

Changing the hypothesis after peeking at the results is generally considered bad practice. This is because it can lead to false-positive results, where the difference appears to be statistically significant but it is not. Fundamentally, it undermines the integrity of the research process. A researcher is supposed to set out their expectations prior to conducting the experimental work so as to avoid such bias.
04

Provide the detailed explanation

In proper practice, it is important to adhere to what was pre-specified in the analysis plan before data is collected and viewed. Making adjustments after having viewed the data leads to bias as it overstates the true evidence in the data. It could lead to the wrong conclusion presenting a distorted view of reality. Also, it can damage trust in scientific studies when the bias is uncovered.

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Most popular questions from this chapter

When a person stands trial for murder, the jury is instructed to assume that the defendant is innocent. Is this claim of innocence an example of a null hypothesis, or is it an example of an alternative hypothesis?

A study is done to see whether a coin is biased. The alternative hypothesis used is two-sided, and the obtained \(z\) -value is 1 . Assuming that the sample size is sufficiently large and that the other conditions are also satisfied, use the Empirical Rule to approximate the \(\mathrm{p}\) -value.

A researcher carried out a hypothesis test using a two-sided alternative hypothesis. Which of the following \(z\) -scores is associated with the smallest p-value? Explain. $$ \text { i. } z=0.50 $$ ii. \(z=1.00\) iii. \(z=2.00\) iv. \(z=3.00\)

According to one source, \(50 \%\) of plane crashes are due at least in part to pilot error (http://www.planecrashinfo.com). Suppose that in a random sample of 100 separate airplane accidents, 62 of them were due to pilot error (at least in part.) a. Test the null hypothesis that the proportion of airplane accidents due to pilot error is not \(0.50\). Use a significance level of \(0.05\). b. Choose the correct interpretation: i. The percentage of plane crashes due to pilot error is not significantly different from \(50 \%\). ii. The percentage of plane crashes due to pilot error is significantly different from \(50 \%\).

Suppose you are testing someone to see whether he or she can tell goat cheese from cheddar cheese. You use many bite-sized cubes selected randomly, half from goat cheese and half from cheddar cheese. The taster is blindfolded. The null hypothesis is that the taster is just guessing and should get about half right. When you reject the null hypothesis when it is actually true, that is often called the first kind of error. The second kind of error is when the null is false and you fail to reject. Report the first kind of error and the second kind of error.
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