1. Initial Post: Identifying a Bias in the Dataset Dataset Overview: Your instructor will provide a dataset along with a description of how the data was collected and the type of knowledge it is expected to help generate. This dataset may have limitations or biases that affect its reliability. Identify a Bias: After reviewing the dataset and its description, identify one specific type of bias related to the data collection process. There are several common types of biases to consider: Sampling Bias: When the sample used for data collection does not represent the larger population. Non-response Bias: Occurs when certain groups or individuals do not respond to a survey or data collection method, affecting the results. Measurement Bias: When data is inaccurately measured or recorded due to flawed instruments or processes. Confirmation Bias: When the data collection or analysis method favors certain outcomes or reinforces pre-existing beliefs. Reporting Bias: When only certain results are reported, or some data is omitted, creating a skewed interpretation. In your post, explain which bias you identified, how it could affect the data's validity, and how it might impact the conclusions that can be drawn from the dataset