Chapter 2: Collecting and Preparing Data | Data Science SSM Study Guide, Sampling & Data Methods 2026

Chapter 2 Collecting and Preparing Data Critical Thinking [2.1, LO 2.1.1] 1. Megan is interested in opening a gaming center that utilizes virtual reality (VR) technology. She wants to gauge the level of interest in VR gaming among the residents of her city. She plans to conduct a survey using a random sampling method and then use this data to determine the potential demand for a VR gaming center in her city. What is the nature of the collected data, experimental or observational? Solution: The nature of the collected data in Megan's survey would be observational. Observational data is data collected by observing and recording information without any intervention or manipulation on the part of the researcher. In Megan's case, she plans to conduct a survey to gauge the level of interest in VR gaming among the residents of her city. The survey participants will provide their opinions, preferences, and demographic information, but Megan will not be manipulating any variables or imposing experimental conditions on them. This indicates that the data collection is observational in nature. [2.2, LO 2.2.2] 3. A high school is conducting a survey to understand the opinions of the school’s students toward the food options available in the cafeteria. They have divided the student population into four groups based on grade level (9th, 10th, 11th, 12th). A random sample of 60 students is selected from each group and asked to rate the cafeteria food on a scale of 1–10. The results are shown in this table. Grade Level Number of Students Average Rating 9th 60 8.1 10th 60 7.4 11th 60 6.3 12th 60 6.1 Table 2.11: High School Survey What sampling technique was used to collect this data? Solution: The sampling technique used to collect this data is stratified sampling. This method, stratified sampling, helps to ensure that the opinions of students from each grade level are represented in the overall results. This resource covers Chapter 2: Collecting and Preparing Data from Data Science SSM. It focuses on how data is gathered, classified, and structured before analysis, with emphasis on sampling techniques and study design. Key topics include observational vs experimental data, population vs sample, random sampling, stratified sampling, bias reduction, survey design, and data preparation techniques used in real-world data science workflows.