Dataset Overview: Your instructor will provide a dataset from Foundation of a Successful Data Project: Identify and Mitigating Bias, along with a description of on how the data was collected, to helps states launch data-driven economic recovery projects as a result of the COVID-19 pandemic, using Recognize Intersectional Systems, and the type of knowledge it is expected to help generate, such as negate social change efforts essential for ensuring good governance and protecting vulnerable populations. 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: o Sampling Bias: When the sample used for data collection does not represent the larger population. o Non-response Bias: Occurs when certain groups or individuals do not respond to a survey or data collection method, affecting the results. o Measurement Bias: When data is inaccurately measured or recorded due to flawed instruments or processes. o Confirmation Bias: When the data collection or analysis method favors certain outcomes or reinforces pre-existing beliefs. o 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