t a null hypothesis that is true.

•We reject a null hypothesis that is false.

• If a researcher does not select the appropriate level of significance (alpha) based upon prior research or industry standard and concludes that the study found a statistical difference when in fact there was no difference, this is referred to as _____.

•Validity

•Reliability

•Type I error

•Type II error

• If you are conducting a study on the impacts of diet and exercise on high blood pressure and you take a proportional sample based upon race/ethnicity, this would be an example of _____.

•Simple random sample

•Cluster sampling

•Stratified sampling

•Convenience sampling

• In a research study, if the sample size is too low and the results do not find a statistical difference when in fact there is a difference, this is referred to as _____.

•Validity

•Reliability

•Type I error

•Type II error

GCU HLT362 Week 3 Quiz Paper

 

 

MORE INFO 

Applied Statistics for Health Care Professionals

Introduction

Statistics is an important tool for healthcare professionals. It can help you design clinical trials and understand the data you’re collecting, but it can also be used to make decisions about your organization and patients. In this post, we will discuss some common statistical tools used in health care research and how they relate to statistics concepts learned in school.

Understand the importance of having a comprehensive understanding of statistical analysis in healthcare.

Statistical analysis is used to help make decisions and understand what is happening in the data. It can also be used to help understand the results of a study or survey. Statistical analysis involves using statistical methods and tools, such as descriptive statistics, inferential statistics (also called hypothesis testing), probability theory and regression analysis, to analyze a set of data. These methods are used to describe characteristics about populations that may not otherwise be apparent from visual inspection alone; they provide insight into patterns among variables by providing information on how much variation there is within each group compared with others; they enable us to assess relationships between two sets of variables without having direct knowledge about either one beforehand (for example whether two variables are positively correlated).

Use appropriate and effective techniques for summarizing data, including graphical and numerical methods.

It is important to use appropriate and effective techniques for summarizing data, including graphical and numerical methods. For example, if you are calculating the average response time of your emergency department, use a bar graph to show how many patients have waited longer than 20 minutes on average. You can also use this information to determine whether there are any areas where service delivery is lacking or if there are specific problems that need addressing.

Perform statistical tests to compare two groups and interpret results.

The scientific method is a process that uses empirical evidence to answer questions.

The steps of a hypothesis test are:

• Formulate a specific, concise question about the population (or sample) you are testing. For example, “Does using health care professionals make patients less satisfied with their treatment?”

• Make observations about your data and then formulate an explanation for what you observed based on these observations (i.e., hypotheses). In this case, we might say that “Patients who received care from health care professionals had lower satisfaction levels than those who did not.”

• Conduct an experiment or other form of research to test your hypothesis by collecting new data so as to confirm or disprove it; if necessary, adjust your hypothesis accordingly before proceeding further with steps 4 through 6 below!

Appreciate the application of statistical confidence intervals when designing research studies in healthcare.

• Appreciate the application of statistical confidence intervals when designing research studies in healthcare.

• Confidence intervals are used to quantify the uncertainty of a statistic. They are particularly useful when you want to determine whether or not two groups show significant differences (i.e., “yes” or “no”). It is also important to be aware that confidence intervals provide information about how precise your results were, which can be useful for planning future studies and assessing the quality of current research findings in your field.

Get an introduction to common statistical tools used in healthcare research, including calculating summary statistics and performing t-tests.

The course will introduce you to the basic concepts of applied statistics and how these are used in healthcare research. You will learn about the importance of having a comprehensive understanding of statistical analysis, as well as how to use appropriate and effective techniques for summarizing data. You’ll also gain an understanding of how statistical tests can be used to compare two groups or examine relationships between variables, including correlation, regression analysis and ANOVA tests. Finally, you’ll learn how results from these analyses are interpreted so that they can be used effectively by your colleagues in their work with patients or clients

Conclusion

We hope that you’ve enjoyed this introduction to the world of statistics in health care and that it has helped you get started on your journey. There is so much more to learn about statistics, but we have only touched the surface here. But remember: The key thing is to stay curious! As long as you keep learning new things, your understanding will never become stagnant.