Question

The National Cancer Institute conducted a 2 -year study to determine whether cancer death rates for areas near nuclear power plants are higher than for areas without nuclear facilities (San Luis Obispo Telegram-Tribune, September 17,1990 ). A spokesperson for the Cancer Institute said, "From the data at hand, there was no convincing evidence of any increased risk of death from any of the cancers surveyed due to living near nuclear facilities. However, no study can prove the absence of an effect." a. Let \(p\) denote the proportion of the population in areas near nuclear power plants who die of cancer during a given year. The researchers at the Cancer Institute might have considered the two rival hypotheses of the form \(H_{0}: p=\) value for areas without nuclear facilities \(H_{a}: p>\) value for areas without nuclear facilities Did the researchers reject \(H_{0}\) or fail to reject \(H_{0} ?\) b. If the Cancer Institute researchers were incorrect in their conclusion that there is no increased cancer risk associated with living near a nuclear power plant, are they making a Type I or a Type II error? Explain. c. Comment on the spokesperson's last statement that no study can prove the absence of an effect. Do you agree with this statement?

Short answer

The researchers from the Cancer Institute failed to reject the null hypothesis \(H_{0}\). If they were incorrect in their conclusion, then they made a Type II error. The statement 'no study can prove the absence of an effect' suggests the inherent limitation in disproving something completely, acknowledging the limitations of any study.

Step-by-step solution

Interpret the Hypotheses

The null hypothesis, \(H_{0}\), represents there is no increased risk of death from cancer in areas near nuclear facilities. In other words, the proportion of the population dying from cancer in these areas is equal to that in areas without nuclear plants. The alternative hypothesis, \(H_{a}\), suggests that the proportion of the population dying from cancer in areas near nuclear facilities is higher than that in areas without nuclear plants.

Evaluate \(H_{0}\) Based on Researchers' Conclusion

The researchers stated that 'there was no convincing evidence of any increased risk of death from the cancers surveyed due to living near nuclear facilities.' This suggests that they failed to reject \(H_{0}\), as there was no evidence to support that the death rate due to cancer near nuclear facilities was any higher.

Identify Potential Type I or Type II Error

If the researchers were wrong and there actually is an increased risk, then they made a Type II error. This is because a Type II error occurs when the null hypothesis is false, but it is not rejected (a 'false negative'). Here, they failed to reject \(H_{0}\) when it might actually be false that the risk is the same.

Reflect on the Statement Regarding Proving Absence of an Effect

The statement that 'no study can prove the absence of an effect' is generally true. In scientific studies, we can gather evidence against a hypothesis or in favor of it but proving something does not exist or has no effect entirely is inherently difficult. Because there are always limitations to a study and variable factors that might not have been accounted for. So, it is more accurate to state evidence supports the absence or presence of an effect rather than an absolute proof of absence.

Key concepts

Type I Error

In hypothesis testing, a Type I error occurs when we incorrectly reject the null hypothesis, even though it is true. Think of it as a false alarm—in other words, saying something has happened when it hasn't.
For example, imagine yelling "fire" in a crowded theater when there is no fire. The consequences can lead to unnecessary panic and disruption.
Within the statistical context, this means you conclude there is an effect or a difference when there actually isn't. Some key points about Type I error:

• It is also called a "false positive".
• The probability of committing a Type I error is denoted by the Greek letter \( \alpha \), which signifies the significance level of the test (e.g., 0.05 or 5%).
• A common strategy to control for Type I errors is setting a lower significance level, but this might increase the likelihood of a Type II error.

Type II Error

A Type II error happens when we fail to reject the null hypothesis when it is, in fact, false. This is akin to a missed opportunity, where we do not notice something that is actually present.
This type of error is particularly tricky because it means we are saying there is no effect when there actually is one — the risk goes unnoticed.When discussing Type II errors, consider:

• Referred to as a "false negative".
• The probability of making a Type II error is denoted by the Greek letter \( \beta \).
• The power of a test (1 - \( \beta \)) is the probability that it will correctly reject a false null hypothesis. More power reduces the chance of a Type II error.

In the example of the cancer risk near nuclear plants, if there really is an increased risk but researchers concluded there wasn't enough evidence, a Type II error would have occurred.

Null Hypothesis

The null hypothesis, often symbolized as \( H_{0} \), serves as the starting point for statistical testing. It suggests that any observed effect in the data is due to chance and that there is no actual difference or relationship.
In simpler terms, it's the hypothesis that predicts "no effect" or "no change."
In the context of the exercise about cancer rates near nuclear plants, the null hypothesis postulated that cancer rates in areas near these plants are the same as in areas without them.Key aspects to understand the null hypothesis:

• It is usually assumed to be true until there is significant evidence against it.
• The objective of hypothesis testing is to make a decision whether to reject or fail to reject \( H_{0} \).
• Failing to reject \( H_{0} \) does not prove it true, it just means there wasn't enough evidence to find a difference.

Alternative Hypothesis

The alternative hypothesis, denoted as \( H_{a} \), stands in opposition to the null hypothesis. It suggests that there is an actual effect or difference, challenging the status quo.
If a significant effect is found in the data, this hypothesis is considered more likely by rejecting the null hypothesis.
In the example provided, the alternative hypothesis is that areas near nuclear facilities have a higher rate of cancer deaths than areas without such facilities.Important features of the alternative hypothesis:

• Adopting \( H_{a} \) means showing evidence in favor that the null hypothesis is not tenable.
• It can be either "one-sided" (specific direction of effect) or "two-sided" (any difference regardless of direction).
• The strength of evidence needed to accept \( H_{a} \) depends on the chosen significance level.

Statistical Significance

Statistical significance is a key concept in hypothesis testing that indicates whether the observed data can be attributed to a specific factor, rather than random chance. It helps researchers decide if the results of an experiment are meaningful enough to draw conclusions. When the results are statistically significant, it means the findings are unlikely to have occurred by chance at the pre-specified significance level. Some crucial points about statistical significance include:

• The significance level, denoted as \( \alpha \), is the threshold for someone to be considered statistically significant (commonly set at 0.05).
• It does not necessarily prove an actual effect, but rather, low probability of results under \( H_{0} \).
• More significant p-values or less than \( \alpha \) lead to rejecting the null hypothesis.

Understanding statistical significance is essential as it guides decisions in light of research findings, like assessing cancer risks near nuclear plants.