SAMPLING TECHNIQUES AND DESIGN OF EXPERIMENTS

What are the advantages of using stratified sampling?

• Decreased accuracy in representing population subgroups

• More accurate representation of population subgroups and improved precision in subgroup comparisons.

• Reduced precision in subgroup comparisons

• Increased bias in sample selection

What are the different types of experimental designs?

• 
  Parallel design

• Within-group design

• 
  Cross-sectional design

• Between-subjects design, Within-subjects design, Mixed design

How can you ensure randomness in simple random sampling?

• Select elements based on alphabetical order

• Assign unique identifiers to each element and use a random number generator for selection.

• 
  Use a predetermined list of elements for sampling

• Choose elements based on their size

What is the purpose of dividing a population into strata in stratified sampling?

• To increase the sample size

• 
  To decrease the accuracy of the results

• To ensure proportional representation of different segments of the population.

• To simplify the data analysis process

What are the limitations of simple random sampling?

• 
  Inaccurate

• Unreliable results

• 
  Inefficient

• Biased results, time-consuming, costly

Explain the difference between One-Way ANOVA and Two-Way ANOVA.

• One-Way ANOVA compares one factor, Two-Way ANOVA compares two factors.

• One-Way ANOVA compares two factors, Two-Way ANOVA compares one factor.

• One-Way ANOVA is used for continuous data, Two-Way ANOVA is used for categorical data.

• One-Way ANOVA is parametric, Two-Way ANOVA is non-parametric.

When is Factorial ANOVA used in statistical analysis?

• Factorial ANOVA is used for analyzing time series data

• Factorial ANOVA is used for comparing means of a single group

• Factorial ANOVA is used for analyzing categorical data

• Factorial ANOVA is used when analyzing the interaction effects between multiple independent variables.

How can you determine the statistical significance of the results in Two-Way ANOVA?

• Use the median instead of the mean

• Calculate the p-value and compare it to the significance level (e.g., 0.05) to determine statistical significance.

• Ignore the p-value and rely on intuition

  
  

• Count the number of variables

What is the null hypothesis in One-Way ANOVA?

• 
  There is no significant difference among the group means.

• There is a significant difference among the group means.

• The sample size is too small to draw conclusions.

• The groups are not related to each other.

What are the challenges faced in implementing random sampling techniques?

• Ignoring non-response bias

• Using a small sample size

• Ensuring randomness, non-response bias, large sample sizes, population representation

• Sampling only from a specific group