What are some common challenges encountered when working with data? (Select all that apply) What are some common challenges encountered when working with data? (Select all that apply) Unrepresentative data Character data Missing values Outliers

When working with data, several common challenges can arise. Here are some of the key issues:

1. Unrepresentative Data: This occurs when the data collected does not accurately reflect the population or phenomenon being studied. It can lead to biased results and incorrect conclusions. Ensuring that data is representative involves careful sampling and consideration of the data collection methods.

2. Character Data: Handling character data can be challenging due to issues like inconsistent formatting, encoding problems, and the need for text preprocessing. This includes tasks such as removing special characters, correcting typos, and standardizing text formats.

3. Missing Values: Missing data is a frequent issue that can skew analysis and lead to inaccurate results. Strategies to address missing values include imputation, where missing values are estimated based on other available data, or using algorithms that can handle missing data.

4. Outliers: Outliers are data points that differ significantly from other observations. They can distort statistical analyses and models. Identifying and deciding how to handle outliers—whether to remove them, transform them, or use robust statistical methods—is crucial for accurate data analysis.

Each of these challenges requires specific strategies and techniques to address effectively, ensuring the integrity and reliability of data analysis.