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Welcome to the Evaluation Techniques MCQs Page

Dive deep into the fascinating world of Evaluation Techniques with our comprehensive set of Multiple-Choice Questions (MCQs). This page is dedicated to exploring the fundamental concepts and intricacies of Evaluation Techniques, a crucial aspect of Human Computer Interaction. In this section, you will encounter a diverse range of MCQs that cover various aspects of Evaluation Techniques, from the basic principles to advanced topics. Each question is thoughtfully crafted to challenge your knowledge and deepen your understanding of this critical subcategory within Human Computer Interaction.

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Check out the MCQs below to embark on an enriching journey through Evaluation Techniques. Test your knowledge, expand your horizons, and solidify your grasp on this vital area of Human Computer Interaction.

Note: Each MCQ comes with multiple answer choices. Select the most appropriate option and test your understanding of Evaluation Techniques. You can click on an option to test your knowledge before viewing the solution for a MCQ. Happy learning!

Evaluation Techniques MCQs | Page 9 of 23

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Q81.
Why is the choice of a statistical test important in experimental analysis?

a.

To manipulate the independent variables and create experimental conditions.

b.

To ensure that participants understand the purpose of the experiment.

c.

To determine the order of conditions presented to participants.

d.

To ensure valid and reliable interpretation of the results.

Discuss
Answer: (d).To ensure valid and reliable interpretation of the results. Explanation:The choice of a statistical test is crucial to ensure the validity and reliability of the results obtained from an experiment.
Q82.
What is the purpose of looking at the data before conducting statistical tests in an experiment?

a.

To determine the statistical significance of the results.

b.

To confirm the accuracy of the experimental design.

c.

To identify outliers or unusual data points.

d.

To decide on the appropriate statistical test to use.

Discuss
Answer: (c).To identify outliers or unusual data points. Explanation:Looking at the data before conducting statistical tests helps identify outliers or unusual data points that could potentially bias the results.
Q83.
Why is saving the original data important in experimental analysis?

a.

It ensures that participants' personal information is protected.

b.

It allows for comparison with the results of previous experiments.

c.

It allows for re-analysis using different methods and verification of results.

d.

It reduces the amount of data that needs to be processed.

Discuss
Answer: (c).It allows for re-analysis using different methods and verification of results. Explanation:Saving the original data allows for re-analysis using different methods, verification of results, and addressing potential issues that may arise in the future.
Q84.
What distinguishes a discrete variable from a continuous variable?

a.

A discrete variable has an upper and lower limit, while a continuous variable does not.

b.

A discrete variable can only take a finite number of values, while a continuous variable can take any value.

c.

A discrete variable can take any value, while a continuous variable can only take a finite number of values.

d.

A discrete variable can take both positive and negative values, while a continuous variable can only take positive values.

Discuss
Answer: (b).A discrete variable can only take a finite number of values, while a continuous variable can take any value. Explanation:A discrete variable can only take a finite number of distinct values, while a continuous variable can take any value within a certain range.
Q85.
In the context of interface experiments, why is the independent variable usually considered discrete?

a.

Continuous variables are more difficult to measure accurately.

b.

Continuous variables provide more detailed information about the experiment.

c.

Discrete variables are easier to manipulate and control in the experimental design.

d.

Discrete variables are more commonly encountered in interface studies.

Discuss
Answer: (c).Discrete variables are easier to manipulate and control in the experimental design. Explanation:Discrete variables are easier to manipulate and control in experimental designs, making them suitable for interface experiments where different design conditions are being compared.
Q86.
What is the primary purpose of conducting statistical tests in experimental analysis?

a.

To validate the experimenter's hypothesis.

b.

To ensure that the experiment is unbiased and accurate.

c.

To determine if the data contains outliers or unusual points.

d.

To assess whether the observed differences between conditions are statistically significant.

Discuss
Answer: (d).To assess whether the observed differences between conditions are statistically significant. Explanation:The primary purpose of conducting statistical tests is to assess whether the observed differences between conditions are statistically significant and not due to random chance.
Q87.
What is the dependent variable in an experiment?

a.

The variable that is manipulated by the experimenter.

b.

The variable that is measured and subject to random experimental variation.

c.

The variable that is controlled to ensure consistent results.

d.

The variable that is kept constant to eliminate variability.

Discuss
Answer: (b).The variable that is measured and subject to random experimental variation. Explanation:The dependent variable is the one that is measured and is subject to random experimental variation. It is what you are observing and trying to analyze in the experiment.
Q88.
Which distribution is commonly associated with parametric tests?

a.

Exponential distribution

b.

Poisson distribution

c.

Uniform distribution

d.

Normal distribution

Discuss
Answer: (d).Normal distribution Explanation:Parametric tests are often used when the data follows a normal distribution, which is the well-known bell-shaped distribution.
Q89.
Under what conditions are parametric tests considered robust?

a.

When the data is not normal but follows a uniform distribution.

b.

When the data is normally distributed.

c.

When the data can be transformed to become approximately normal.

d.

When the data is skewed.

Discuss
Answer: (c).When the data can be transformed to become approximately normal. Explanation:Parametric tests are considered robust when the data can be transformed to become approximately normal. This means they can provide reasonable results even when the data are not precisely normal.
Q90.
What is the primary difference between parametric and non-parametric tests?

a.

Parametric tests are used for discrete data, while non-parametric tests are used for continuous data.

b.

Parametric tests make assumptions about the distribution of data, while non-parametric tests make no assumptions.

c.

Parametric tests are more resistant to outliers than non-parametric tests.

d.

Parametric tests are more powerful than non-parametric tests.

Discuss
Answer: (b).Parametric tests make assumptions about the distribution of data, while non-parametric tests make no assumptions. Explanation:Parametric tests make assumptions about the distribution of data, such as normality, while non-parametric tests make no assumptions about the distribution and are based purely on the ranking of data.
Page 9 of 23

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