Data Collection Bias

In data science, what is bias in datasets typically referred to?

• Preference for certain data types

• Unintentional errors in data collection

• Overfitting of machine learning models

• Lack of diversity in data representation

What does sampling bias in datasets imply?

• The dataset contains too few samples

• The dataset is biased towards certain groups

• The dataset is collected from a single source

• The dataset is uniformly distributed

Which of the following can help mitigate bias in datasets?

• Including only data from a single source

• Collecting data from diverse sources

• Ignoring outliers in the dataset

• Using a small sample size

What does measurement bias refer to in datasets?

• Bias introduced during data collection

• Errors in the measurement process

• Unintentional bias in the analysis

• Lack of sufficient data

Which step in the data science process is critical for identifying bias in datasets?

• Data preprocessing

• Exploratory data analysis

• Model training

• Model evaluation

What is the role of data engineers in addressing bias in datasets?

• Selecting machine learning algorithms

• Collecting and preprocessing data

• Training and fine-tuning models

• Deploying machine learning models

Which term refers to bias introduced due to systematic errors in data collection?

• Sampling bias

• Measurement bias

• Interpretation bias

• Confirmation bias

What does dataset diversity aim to achieve?

• Including data from a single source

• Ignoring certain data points

• Representing various perspectives and demographics

• Excluding outliers

Why is it important to address bias in datasets?

• It ensures the accuracy of machine learning models

• It increases computational efficiency

• It reduces the need for data preprocessing

• It improves the interpretability of results