What is a Type I error in data analysis?

• Rejecting a true null hypothesis

• Accepting a false null hypothesis

• Failing to reject a true null hypothesis

• Failing to reject a false null hypothesis

What is a Type II error in data analysis?

• Failing to reject a false null hypothesis

• Rejecting a true null hypothesis

• Accepting a true alternative hypothesis

• Failing to accept a true null hypothesis

Define reliability in the context of data analysis.

• Reliability in data analysis refers to the consistency and dependability of the data and the results obtained from it. It indicates the extent to which the same data collection methods and analysis procedures yield similar results under consistent conditions.

• Reliability in data analysis refers to the accuracy and precision of the data collected, ensuring that it is free from errors and biases.

• Reliability in data analysis is about the ability to predict future outcomes based on historical data trends.

• Reliability in data analysis involves the use of advanced statistical techniques to enhance data interpretation.

Define validity in the context of data analysis.

• Validity refers to the extent to which a concept, conclusion, or measurement accurately reflects the intended meaning or truth in the context of data analysis. It assesses whether the data collected truly represents the phenomenon being studied and whether the analysis accurately captures the relationships and patterns within the data.

• Validity refers to the ability to replicate the results of a study using different data sets.

• Validity is the degree to which data is free from errors and inconsistencies.

• Validity is the process of collecting data from various sources to ensure comprehensive analysis.

What is bias in data analysis?

• A systematic error that leads to incorrect conclusions

• A random error that occurs by chance

• An error that occurs due to data entry mistakes

• A method to ensure data accuracy

What are confidence limits in data analysis?

• Confidence limits are the range within which a population parameter is estimated to lie with a certain level of confidence.

• Confidence limits are the exact values of a population parameter.

• Confidence limits are the boundaries of a data set.

• Confidence limits are the average values of a data set.

What are significance levels in data analysis?

• Significance levels are thresholds used to determine the probability of rejecting a null hypothesis when it is true.

• Significance levels are the actual values obtained from a statistical test.

• Significance levels are the same as confidence intervals in data analysis.

• Significance levels are used to measure the central tendency of a dataset.

What is correlation in data analysis?

• A measure of the relationship between two variables

• A method to calculate the average

• A technique to sort data

• A way to visualize data

What is dispersion in data analysis?

• The extent to which data points in a data set vary from the average value

• The process of collecting data from various sources

• The method of organizing data in a structured format

• The technique of visualizing data using graphs

Explain how to choose the correct statistical test for data analysis.

• By considering the type of data and research question

• By randomly selecting a test

• By using the most popular test

• By choosing the test with the shortest name