Final answer:
Common problems faced by data analysts include lack of data, biased data, overfitting, underfitting, and data breaches. Proper sample size and randomization are important in avoiding chance errors and bias. Analysts must critically evaluate statistical studies and be aware of potential fraud.
Step-by-step explanation:
Data analysts commonly face a variety of challenges when working with data. Some of the common problems faced by data analysts include a lack of data, which makes it difficult to perform certain types of analysis or draw reliable conclusions. Analysts might also encounter biased data, which can lead to incorrect conclusions about a population if the data collection method is not appropriately randomized. For example, a survey of students conducted only during noon lunchtime hours would be biased against those who don't have lunch at that time.
Another issue is overfitting, which occurs when a statistical model describes random error or noise instead of the underlying relationship. Conversely, underfitting happens when a model is too simple and fails to capture the complexity of the data. Data breaches are also a real-world problem for data analysts, as they represent a failure to protect data integrity and can lead to significant consequences both for the individuals affected and the organizations responsible for the data.
When analyzing and interpreting data, such as raw data from scientific studies, analysts need to evaluate the presented statistical studies critically, ensure proper sample size to avoid chance errors, and use correct sampling methods to prevent bias. Some researchers might even commit statistical fraud by stopping their data collection prematurely once sufficient data has been collected to superficially support their hypothesis.