Question

In which way could the results of an experiment have bias?
when samples sizes are large
an outlier data point is present
when sample sizes are chosen at random
data that has been evaluated for accuracy

Answer 1

"An outlier data point is present."

Explanation: outliers can skew summary statistics (especially the mean) and lead to misleading conclusions if not identified and handled appropriately. Large sample sizes and random sampling tend to reduce random error and selection bias, and evaluating data for accuracy reduces error rather than creating bias. To mitigate outlier effects use robust statistics (median, trimmed mean), investigate and correct measurement errors, or justify and document any exclusions.