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Pesticides and ADHD Are children with higher exposure to pesticides more likely to develop ADHD (attention-deficit/hyperactivity disorder)? In one study, authors measured levels of urinary dialkyl phosphate (DAP, a common pesticide) concentrations and ascertained ADHD diagnostic status (Yes/No) for 1139 children who were representative of the general US population. \(^{7}\) The subjects were divided into two groups based on high or low pesticide concentrations, and we compare the proportion with ADHD in each group. (a) Define the relevant parameter(s) and state the null and alternative hypotheses. (b) In the sample, children with high pesticide levels were more likely to be diagnosed with ADHD. Can we necessarily conclude that, in the population, children with high pesticide levels are more likely to be diagnosed with ADHD? (Whether or not we can make this generalization is, in fact, the statistical question of interest.) (c) In the study, evidence was found to support the alternative hypothesis. Explain what that means in the context of pesticide exposure and ADHD?

Short Answer

Expert verified
The parameters are the proportions of children with or without ADHD among high and low pesticide exposure groups. The null hypothesis states no difference in ADHD occurrence across these groups, while the alternative indicates higher likelihood with greater exposure. Generalizing the conclusion drawn from the sample to the whole population requires statistical support such as hypothesis testing. Evidence favoring the alternative hypothesis indicates a greater likelihood of children with high exposure developing ADHD, although causation isn't confirmed.

Step by step solution

01

Define the parameters, null and alternative hypotheses

The parameters of interest are the proportions of children with ADHD in the groups with high and low pesticide exposure, usually denoted by \(p_1\) and \(p_2\). The null hypothesis could be that the proportion of children who develop ADHD is the same regardless of pesticide exposure level, i.e., \(H_0: p_1 = p_2\). The alternative hypothesis, however, would propose a difference in ADHD occurrence between the groups, specifically stating that children with high pesticide exposure are more likely to develop ADHD. Therefore, the alternative hypothesis could be \(H_a: p_1 > p_2\).
02

Discussing the possibility of generalization

Although the study found that children with high pesticide levels were more likely to be diagnosed with ADHD in the sample, it's not necessarily appropriate to conclude the same about the overall population. This reservation is due to the possibility of sampling bias or errors in the study, or simply the chance variation in samples. Hence, it would require further statistical testing, such as hypothesis testing to draw such a population-wide conclusion.
03

Interpretation of the evidence in favor of the alternative hypothesis

If there's evidence to support the alternative hypothesis, it implies that the study's results are statistically significant and not likely due to chance. In this context, it means that children with higher pesticide exposure are more likely to develop ADHD than those with lower exposure. However, note that this does not necessarily prove a causal relationship, as correlation does not always imply causation.

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Key Concepts

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

Statistical Significance
Statistical significance is a core concept in hypothesis testing. It helps us determine if a result from a sample reflects a genuine effect in the population or if it could have occurred by chance.
In the context of the exercise concerning pesticide exposure and ADHD, statistical significance would mean that the observed difference in ADHD rates between children exposed to high levels of pesticides and those with low exposure is unlikely to be due to random chance.
  • When authors find "evidence to support the alternative hypothesis," they imply that the difference between the groups in the study is statistically significant.
  • This finding suggests a high likelihood that there's a real association between pesticide exposure and ADHD in the population, beyond just the sample examined.
It's crucial to note that statistical significance alone doesn't account for the magnitude of the effect or its practical implications in real-life scenarios.
Pesticide Exposure
Pesticide exposure, particularly through the chemical compounds found in common pesticides like dialkyl phosphate (DAP), is a concern for potential health risks. In this study, the focus is on whether exposure to higher levels of these compounds can increase the likelihood of ADHD in children.
  • The study divides children into two groups: high and low pesticide concentrations, and observes the prevalence of ADHD in each.
  • Higher pesticide exposure is hypothesized to result in a greater incidence of ADHD, leading to careful testing of this hypothesis through observation and data collection.
Understanding the risks associated with pesticide exposure is important not only for forming health guidelines but also for informing public health policies aimed at reducing unnecessary exposure, especially in vulnerable populations like children.
ADHD
Attention-deficit/hyperactivity disorder (ADHD) is a common neurodevelopmental disorder affecting children and can persist into adulthood. It is characterized by symptoms like inattentiveness, hyperactivity, and impulsiveness.
In the study provided, ADHD is the primary condition being studied in relation to pesticide exposure. Researchers are exploring whether increased pesticide exposure has any correlation with a heightened prevalence of ADHD in children.
  • By focusing on ADHD diagnostic status in relation to pesticide levels, the study aims to uncover potential environmental contributors to the disorder.
  • This research is significant as understanding factors that may exacerbate or contribute to ADHD can lead to better management and preventative strategies.
Ultimately, connecting environmental exposure like pesticides to ADHD hopesto enhance our comprehension of various influences on this disorder, steering both scientific inquiry and clinical practices.

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Most popular questions from this chapter

Flaxseed and Omega-3 Exercise 4.30 on page 271 describes a company that advertises that its milled flaxseed contains, on average, at least \(3800 \mathrm{mg}\) of ALNA, the primary omega-3 fatty acid in flaxseed, per tablespoon. In each case below, which of the standard significance levels, \(1 \%\) or \(5 \%\) or \(10 \%,\) makes the most sense for that situation? (a) The company plans to conduct a test just to double-check that its claim is correct. The company is eager to find evidence that the average amount per tablespoon is greater than 3800 (their alternative hypothesis), and is not really worried about making a mistake. The test is internal to the company and there are unlikely to be any real consequences either way. (b) Suppose, instead, that a consumer organization plans to conduct a test to see if there is evidence against the claim that the product contains at least \(3800 \mathrm{mg}\) per tablespoon. If the organization finds evidence that the advertising claim is false, it will file a lawsuit against the flaxseed company. The organization wants to be very sure that the evidence is strong, since if the company is sued incorrectly, there could be very serious consequences.

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Influencing Voters When getting voters to support a candidate in an election, is there a difference between a recorded phone call from the candidate or a flyer about the candidate sent through the mail? A sample of 500 voters is randomly divided into two groups of 250 each, with one group getting the phone call and one group getting the flyer. The voters are then contacted to see if they plan to vote for the candidate in question. We wish to see if there is evidence that the proportions of support are different between the two methods of campaigning. (a) Define the relevant parameter(s) and state the null and alternative hypotheses. (b) Possible sample results are shown in Table 4.3 . Compute the two sample proportions: \(\hat{p}_{c},\) the proportion of voters getting the phone call who say they will vote for the candidate, and \(\hat{p}_{f},\) the proportion of voters getting the flyer who say they will vote for the candidate. Is there a difference in the sample proportions? (c) A different set of possible sample results are shown in Table 4.4. Compute the same two sample proportions for this table. (d) Which of the two samples seems to offer stronger evidence of a difference in effectiveness between the two campaign methods? Explain your reasoning. $$ \begin{array}{lcc} \hline & \begin{array}{c} \text { Will Vote } \\ \text { Sample A } \end{array} & \text { for Candidate } & \begin{array}{l} \text { Will Not Vote } \\ \text { for Candidate } \end{array} \\ \hline \text { Phone call } & 152 & 98 \\ \text { Flyer } & 145 & 105 \\ \hline \end{array} $$ $$ \begin{array}{lcc} \text { Sample B } & \begin{array}{c} \text { Will Vote } \\ \text { for Candidate } \end{array} & \begin{array}{c} \text { Will Not Vote } \\ \text { for Candidate } \end{array} \\ \hline \text { Phone call } & 188 & 62 \\ \text { Flyer } & 120 & 130 \\ \hline \end{array} $$

In a test to see whether there is a positive linear relationship between age and nose size, the study indicates that " \(p<0.001\)."

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