Warning: foreach() argument must be of type array|object, bool given in /var/www/html/web/app/themes/studypress-core-theme/template-parts/header/mobile-offcanvas.php on line 20

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

Expert verified
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

01

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.
02

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.
03

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.
04

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.

Unlock Step-by-Step Solutions & Ace Your Exams!

  • Full Textbook Solutions

    Get detailed explanations and key concepts

  • Unlimited Al creation

    Al flashcards, explanations, exams and more...

  • Ads-free access

    To over 500 millions flashcards

  • Money-back guarantee

    We refund you if you fail your exam.

Over 30 million students worldwide already upgrade their learning with Vaia!

Key Concepts

These are the key concepts you need to understand to accurately answer the question.

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.

One App. One Place for Learning.

All the tools & learning materials you need for study success - in one app.

Get started for free

Most popular questions from this chapter

In \(2006,\) Boston Scientific sought approval for a new heart stent (a medical device used to open clogged arteries) called the Liberte. This stent was being proposed as an alternative to a stent called the Express that was already on the market. The following excerpt is from an article that appeared in The Wall Street Journal (August 14,2008 ): Boston Scientific wasn't required to prove that the Liberte was 'superior' than a previous treatment, the agency decided - only that it wasn't "inferior' to Express. Boston Scientific proposed - and the FDA okayed - a benchmark in which Liberte could be up to three percentage points worse than Express meaning that if \(6 \%\) of Express patients' arteries reclog, Boston Scientific would have to prove that Liberte's rate of reclogging was less than \(9 \%\). Anything more would be considered 'inferior.'... In the end, after nine months, the Atlas study found that 85 of the patients suffered reclogging. In comparison, historical data on 991 patients implanted with the Express stent show a \(7 \%\) rate. Boston \(S\) cientific then had to answer this question: Could the study have gotten such results if the Liberte were truly inferior to Express?" Assume a \(7 \%\) reclogging rate for the Express stent. Explain why it would be appropriate for Boston Scientific to carry out a hypothesis test using the following hypotheses: \(H_{0}: p=.10\) \(H_{a}: p<.10\) where \(p\) is the proportion of patients receiving Liberte stents that suffer reclogging. Be sure to address both the choice of the hypothesized value and the form of the alternative hypothesis in your explanation.

A manufacturer of hand-held calculators receives large shipments of printed circuits from a supplier. It is too costly and time-consuming to inspect all incoming circuits, so when each shipment arrives, a sample is selected for inspection. Information from the sample is then used to test \(H_{0}: p=.01\) versus \(H_{a}: p>.01\), where \(p\) is the actual proportion of defective circuits in the shipment. If the null hypothesis is not rejected, the shipment is accepted, and the circuits are used in the production of calculators. If the null hypothesis is rejected, the entire shipment is returned to the supplier because of inferior quality. (A shipment is defined to be of inferior quality if it contains more than \(1 \%\) defective circuits.) a. In this context, define Type I and Type II errors. b. From the calculator manufacturer's point of view, which type of error is considered more serious? c. From the printed circuit supplier's point of view, which type of error is considered more serious?

A certain pen has been designed so that true average writing lifetime under controlled conditions (involving the use of a writing machine) is at least 10 hours. A random sample of 18 pens is selected, the writing lifetime of each is determined, and a normal probability plot of the resulting data supports the use of a one-sample \(t\) test. The relevant hypotheses are \(H_{0}: \mu=10\) versus \(H_{a}: \mu<10 .\) a. If \(t=-2.3\) and \(\alpha=.05\) is selected, what conclusion is appropriate? b. If \(t=-1.83\) and \(\alpha=.01\) is selected, what conclusion is appropriate? c. If \(t=0.47,\) what conclusion is appropriate?

The article "Fewer Parolees Land Back Behind Bars" (Associated Press, April II, 2006 ) includes the following statement: "Just over \(38 \%\) of all felons who were released from prison in 2003 landed back behind bars by the end of the following year, the lowest rate since \(1979 . "\) Explain why it would not be necessary to carry out a hypothesis test to determine if the proportion of felons released in 2003 was less than .40 .

Explain why the statement \(\bar{x}=50\) is not a legitimate hypothesis.

See all solutions

Recommended explanations on Math Textbooks

View all explanations

What do you think about this solution?

We value your feedback to improve our textbook solutions.

Study anywhere. Anytime. Across all devices.

Sign-up for free