Final answer:
Bias in an experimental design is linked to systematic error in estimating a population parameter. Without comparing to the true population parameters or understanding the sampling process thoroughly, it's impossible to conclude bias. The answer is that bias cannot be determined from the information provided.
Step-by-step explanation:
When examining whether there is any bias in the experimental design of manufacturing 1.5-inch screws with a sample of 200 screws having an average length of 1.476 inches and a standard deviation of 0.203 inches, we need to consider if the sample was collected in a way that is representative of the entire population of screws. Bias in an experimental design refers to a systematic error that results in an estimate that is systematically different from the true population parameter being measured. In this case, to determine if there is bias, we must examine the process in which the sample was collected and compare it to the true population parameters if known.
The additional information provided indicates that the screws were randomly selected from a local home repair store. However, without a reference to the true population mean and the expected tolerance levels, it is impossible to concretely conclude that the design is biased simply based on the sample statistics given. If the sample mean is significantly different from the manufacturer's claimed mean, this could suggest possible bias, but further investigation into the sampling technique and population parameters would be required.
The answer, therefore, would be C) Bias cannot be determined from the information provided.