Statistics Randomization

What are the key components of experimental design in statistics data collection?

• Hypothesis, correlation, causation, and inference

• Qualitative data, quantitative data, sample size, and confidence interval

• Survey, interview, observation, and secondary data

• Control group, experimental group, randomization, replication, and blocking

What is the role of blocking in experimental design in statistics data collection?

• Blocking has no role in experimental design in statistics data collection.

• Blocking helps in increasing the sample size and thus, improving the reliability of the results.

• Blocking helps in making the data collection process faster.

• Blocking is used to reduce the impact of confounding variables by grouping similar experimental units together and then randomizing within these groups.

The following example represents which type of sampling?
Workers in a factory are divided into males and females. A random sample of size 14 is then drawn from each of the groups.

• Convenience

• Cluster

• Systematic

• Stratified

Drawing conclusions about a population based on sample data is known as

• Descriptive Statistics

• Inferential Statistics

Select all the options that would produce a random sample.

• Survey the first 50 people that walk in the doors.

• Survey everyone at his jazz band practice.

• Survey two randomly chosen students in every homeroom.

• Assign each student in the school a number, randomly choose 50 numbers, and survey those students.

• Survey every third student that he sees on his walk to school.

Sample in which each individual or object in the entire population has an equal chance of being selected.

• Simple Event

• Sample

• Random Sample

• Scatter Plot

A sample in which each individual or object in the entire population has an equal chance of being selected.

• biased sample

• population

• random sample

• sample

The purpose of randomization in an experiment is to

• make larger groups

• reduce bias

• control the environment

• get more varied results

Each individual is given a fair, random chance of selection

• bias

• randomization

• parameter

• statistic

Values calculated from sampled data that estimates a population

• parameter

• statistic

• randomization

• sample size