Final answer:
A good example of sample bias is when a model designed to recognize pets is only given photos of dogs, which does not represent the full range of pet types. Sample bias occurs when the chosen samples are not random and hence not representative of the entire population. Avoiding omissions of relevant data is key to preventing bias.
Step-by-step explanation:
A good example of sample bias among the provided options is A: Your model is trained to recognize pets, but you only give it photos of dogs. This is because the sample does not represent all types of pets that exist, as it is limited to only one type: dogs. Thereby, members of the larger population, in this case 'pets', which includes cats, birds, and other animals, do not have an equally likely chance of being chosen.
Bias occurs if the sample is not selected randomly with respect to a variable in the study. This leads to scenarios where the model or study might draw incorrect conclusions about the population under review. To avoid sample bias, it is crucial to ensure that the sample is representative of the entire population and that every member of the population has an equal opportunity to be included in the sample.
Furthermore, it's important to avoid intentionally omitting relevant data because doing so can create a biased sample that would not accurately represent the population as a whole. Sampling bias can have a significant impact on the validity of the study and can mislead decision-making based on the model or research outcomes.