Q1. What is statistics (according to lecture and your workbook)?

Background

Topic: Definition of Statistics

This question is testing your understanding of what statistics is and its role in data analysis and decision making.

Key Terms:

• Statistics: The science of collecting, analyzing, interpreting, and presenting data.

• Data: Information collected from observations, surveys, or experiments.

Step-by-Step Guidance

1. Recall the formal definition of statistics from your lecture notes and workbook.

2. Think about the main purposes of statistics: describing data, making inferences, and supporting decision making.

3. Consider how statistics is used in real-world contexts, such as business, science, and social research.

Try solving on your own before revealing the answer!

Q2. What are statistics vs. parameters?

Background

Topic: Statistics vs. Parameters

This question is testing your ability to distinguish between statistics (sample measures) and parameters (population measures).

Key Terms:

• Statistic: A numerical measure that describes a characteristic of a sample.

• Parameter: A numerical measure that describes a characteristic of a population.

Step-by-Step Guidance

1. Recall the definitions of sample and population.

2. Identify which measures are calculated from samples and which from populations.

3. Think about examples: sample mean vs. population mean.

Try solving on your own before revealing the answer!

Q3. Know difference b/w quantitative variables and qualitative variables.

Background

Topic: Types of Variables

This question is testing your understanding of the distinction between quantitative (numerical) and qualitative (categorical) variables.

Key Terms:

• Quantitative Variable: A variable that can be measured numerically (e.g., height, weight).

• Qualitative Variable: A variable that describes categories or qualities (e.g., gender, color).

Step-by-Step Guidance

1. Recall the definitions of quantitative and qualitative variables.

2. Think about examples of each type from your textbook or lecture.

3. Consider how each type of variable is used in statistical analysis.

Try solving on your own before revealing the answer!

Q4. Know difference b/w discrete and continuous variables.

Background

Topic: Types of Quantitative Variables

This question is testing your ability to distinguish between discrete and continuous variables within quantitative data.

Key Terms:

• Discrete Variable: A quantitative variable that takes on distinct, separate values (often counts).

• Continuous Variable: A quantitative variable that can take on any value within a range (often measurements).

Step-by-Step Guidance

1. Recall the definitions of discrete and continuous variables.

2. Think about examples: number of students (discrete), height (continuous).

3. Consider how each type is represented in data sets.

Try solving on your own before revealing the answer!

Q5. Know difference b/w levels of measurement: nominal, ordinal, interval, and ratio.

Background

Topic: Levels of Measurement

This question is testing your understanding of the four levels of measurement used in statistics.

Key Terms:

• Nominal: Categories without order (e.g., colors).

• Ordinal: Categories with order (e.g., rankings).

• Interval: Ordered, equal intervals, no true zero (e.g., temperature in Celsius).

• Ratio: Ordered, equal intervals, true zero (e.g., height, weight).

Step-by-Step Guidance

1. Recall the definitions and characteristics of each level of measurement.

2. Think about examples for each level.

3. Consider how the levels affect statistical analysis and interpretation.

Try solving on your own before revealing the answer!

Q6. What are the differences between observational studies vs. experiments?

Background

Topic: Types of Statistical Studies

This question is testing your ability to distinguish between observational studies and experiments.

Key Terms:

• Observational Study: Researchers observe subjects without intervention.

• Experiment: Researchers manipulate variables to observe effects.

Step-by-Step Guidance

1. Recall the definitions of observational studies and experiments.

2. Think about how data is collected in each type.

3. Consider the implications for causality and bias.

Try solving on your own before revealing the answer!

Q7. Know the difference between random, systematic, stratified sampling, cluster sampling, and convenience sampling.

Background

Topic: Sampling Methods

This question is testing your understanding of different sampling techniques used in statistics.

Key Terms:

• Random Sampling: Every member has an equal chance of being selected.

• Systematic Sampling: Select every nth member from a list.

• Stratified Sampling: Divide population into strata and sample from each.

• Cluster Sampling: Divide population into clusters, randomly select clusters.

• Convenience Sampling: Select members who are easiest to reach.

Step-by-Step Guidance

1. Recall the definitions and procedures for each sampling method.

2. Think about the advantages and disadvantages of each method.

3. Consider examples where each method might be used.

Try solving on your own before revealing the answer!

Q8. What are the differences between descriptive statistics vs. inferential statistics?

Background

Topic: Types of Statistics

This question is testing your ability to distinguish between descriptive and inferential statistics.

Key Terms:

• Descriptive Statistics: Summarize and describe data.

• Inferential Statistics: Make predictions or inferences about a population based on sample data.

Step-by-Step Guidance

1. Recall the definitions of descriptive and inferential statistics.

2. Think about examples: mean, median (descriptive); hypothesis testing (inferential).

3. Consider how each type is used in analysis.

Try solving on your own before revealing the answer!