Why handling missing data matters in Pandas - Quick Recap

Recall & Review

beginner

What is missing data in a dataset?

Missing data refers to the absence of values in some parts of a dataset where data should be present. It can happen due to errors, non-response, or data corruption.

beginner

Why is it important to handle missing data before analysis?

Handling missing data is important because it can bias results, reduce accuracy, and cause errors in calculations or models if left untreated.

intermediate

What can happen if missing data is ignored in a machine learning model?

Ignoring missing data can lead to wrong predictions, poor model performance, and unreliable insights because the model learns from incomplete information.

beginner

Name two common ways to handle missing data.

Two common ways are: 1) Removing rows or columns with missing values, 2) Filling missing values with a statistic like mean, median, or a fixed value.

intermediate

How does missing data affect the quality of data analysis?

Missing data can reduce the quality by causing biased estimates, reducing sample size, and making the analysis less reliable or valid.

What is a common consequence of ignoring missing data in analysis?

A Biased results

B Faster computation

C More data collected

D Improved accuracy

Ignoring missing data often leads to biased results because the analysis is based on incomplete information.

Which method is NOT a way to handle missing data?

A Removing rows with missing values

B Ignoring missing values completely

C Using median to fill missing values

D Filling missing values with mean

Ignoring missing values completely can cause errors and bias; handling them is necessary.

Why can missing data reduce the sample size?

A Because rows with missing data may be removed

B Because missing data is duplicated

C Because missing data adds new rows

D Because missing data improves data quality

Removing rows with missing data reduces the number of samples available for analysis.

Which of these is a simple way to fill missing data?

A Replace with random numbers

B Replace with zeros only

C Replace with text labels

D Replace with mean value

Replacing missing values with the mean is a common simple method to fill missing data.

What does missing data often indicate in real-life datasets?

A Perfect data collection

B Extra information

C Errors or gaps in data collection

D Data duplication

Missing data usually indicates errors, non-response, or gaps in how data was collected.

Explain why handling missing data is important before doing any data analysis.

Think about how incomplete data affects the story the data tells.

Describe two common methods to handle missing data and when you might use them.

Consider simple ways to fix or avoid missing data problems.