Final answer:
The statement is true, as larger sample sizes decrease overreliance by improving the reliability of results. The central limit theorem confirms this relationship, but it is also critical to use unbiased sampling techniques.
Step-by-step explanation:
True. The statement that 'the risk of overreliance is inversely related to sample size in an attributes sampling application' is indeed correct. In the context of attributes sampling, the larger the sample size, the more confidence one can have in the results.
The central limit theorem supports this by stating that with a larger sample size, the sampling distribution of the means will closely approximate a normal distribution. This reflects that larger samples can better model the population and are likely to be more reliable.
Larger samples reduce sampling error and lead to more robust statistical data. However, it's also important to note that a biased sampling technique may still lead to unrepresentative results, regardless of the sample size. Therefore, while larger sample sizes improve reliability, they must be accompanied by appropriate sampling methods to ensure that the sample is representative of the population.