Final answer:
Analyzing questions for bias involves identifying if they are leading or based on stereotypes, which compromise their neutrality. Anchoring and confirmation biases are examples of biases that can occur in question design. In data analysis, choosing the correct measure of center like mean or median is based on the shape of the data distribution.
Step-by-step explanation:
When analyzing questions for bias, it is important to discern whether the wording or framing of the question leads the person being questioned towards a certain answer or whether it is neutral and unbiased. A biased question would typically suggest a right or wrong answer or could be leading by fixating on a single aspect of a problem, such as in anchoring bias. For example, regarding the type of bias involving fixation on a single trait, the answer is anchoring bias. The same goes for relying on false stereotypes for making decisions, which is an example of confirmation bias. In terms of a scientific poll design, a leading question is generally avoided as it can introduce bias, whereas a random sample is part of a good poll design to ensure unbiased representation. Finally, when examining the shape of data for determining the appropriate measure of center (mean, median, mode), it's essential to consider the data's distribution to avoid skewed results. For example, the mean is more appropriate for normal distributions, while the median may better represent the center in a skewed distribution.