Final answer:
Larger sample sizes tend to decrease consumer's risk by providing a more accurate representation of the population, leading to narrower confidence intervals and reduced sampling variability. However, biases in large samples can occur, so proper sampling techniques must also be used.
Step-by-step explanation:
Increasing the sample size in statistical analyses tends to decrease consumer's risk because it provides a more accurate representation of the population. A larger sample size decreases the error bound, leading to a narrower confidence interval, which indicates greater precision of the estimate. Consequently, this reduces the chance of making incorrect inferences about the population, hence lowering consumer's risk. Larger samples are more likely to yield reliable results and thereby decrease the variability between samples, often referred to as sampling variability.
Specifically, it reduces the risk of encountering unusual responses that might skew the results. However, it's important to note that beyond a certain point, increases in sample size result in diminishing returns in terms of improved accuracy and may not be cost-effective. While larger samples are generally better, one must also be mindful that large sample bias can occur, especially in cases where the sampling method is flawed, such as in self-selected internet surveys. Thus, larger sample sizes can decrease consumers' risk but should be coupled with proper sampling techniques to ensure reliability and validity of the results.