HLT 362V Module 3 Hypothesis Testing

HLT 362V Module 3 Hypothesis Testing

HLT362V

Problem 1

1. There is a new drug that is used to treat leukemia. The following data represents the remission time in weeks for a random sample of 21 patients using the drug.

Problem 2

2. We wish to test the claim that the mean body mass index (BMI) of men is equal to the mean BMI of women. Use the data to the right to test this claim.

HLT 362V Module 3 Exercise 31

HLT362V

What are the two groups whose results are reflected by the t ratios in Tables 2 and 3?

Which t ratio in Table 2 represents the greatest relative or standardized difference between the pretest and 3 months outcomes? Is this t ratio statistically significant? Provide a rationale for your answer.

Which t ratio listed in Table 3 represents the smallest relative difference between the pretest and 3 months? Is this t ratio statistically significant? What does this result mean?

What are the assumptions for conducting a t-test for dependent groups in a study? Which of these assumptions do you think were met by this study?

Compare the 3 months and 6 months t ratios for the variable Exercise from Table 3. What is your conclusion about the long-term effect of the health-promotion intervention on Exercise in this study?

What is the smallest, significant t ratio listed in Table 2? Provide a rationale for your answer.

Why are the larger t ratios more likely to be statistically significant?

Did the health-promotion program have a statistically significant effect on Systolic blood pressure (BP) in this study? Provide a rationale for your answer.

Examine the means and standard deviations for Systolic BP at pretest, 3 months (completion of the treatment), and 6 months. What do these results indicate? Are these results clinically important? Provide a rationale for your answer.

Is this study design strong or weak? Provide a rationale for your answer.

Would you, as a health care provider, implement this intervention at your facility based on the Total Risk Score results? Provide a rationale for your answer.

 

 

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Hypothesis Testing

Introduction

In statistics and data science, hypothesis testing is used to determine whether the null hypothesis or alternative hypothesis is true based on samples. It is also used to estimate the probability of certain parameter values.

Hypothesis testing is used to estimate the probability of certain parameter values.

Hypothesis testing is used to determine the probability of a certain parameter value. The null hypothesis and alternative hypothesis are used to determine this probability. The null hypothesis is the default hypothesis and the alternative hypothesis is the opposite of that.

The following example shows how to use these two concepts:

Let’s say someone wants to test whether there are more red cars than blue cars on their street at any given time during an afternoon (a cross-sectional study). They will observe 500 cars over 3 hours and count which colors were present in each car (i.e., “red”, “blue”, etc.). If they find that 51% of all observed cars had one color but only 4% had another, then we could say with 95% confidence that more red cars than blue ones were present during this hour (this would be called a positive result). If 50% were found with one color but 5% were found with another, then again we could state that 95% confidence exists in favor of our finding a higher number of reds than blues…

Steps of hypothesis testing:

• State the null hypothesis.

• State your alternative hypothesis.

• State the significance level.

• Compute a test statistic, which you can use to compute p-values and reject or fail to reject the null hypothesis (if this happens, then we have found evidence against our null hypothesis).

Hypothesis testing is used to determine whether the null hypothesis or alternative hypothesis is true based on samples.

Hypothesis testing is used to determine whether the null hypothesis or alternative hypothesis is true based on samples. The null hypothesis is a statement that the population mean is equal to some value, while the alternative hypothesis states that it’s not. The sample means of your data should be close enough to this value for you to reject the null hypothesis and thus support your original theory.

For example: if you’re testing whether salespeople are able to increase their sales by 20% over last year’s total (this would be your null), then you want your sample mean—the average amount of each product sold by all customers—to be as close as possible yet still far from its actual value due how much variability there might be between individual customers’ purchases. In this case, even though there might only be one customer with $100 worth of product bought at once (and therefore unlikely affected by any one employee making changes), those who purchased more than $100 worth could still skew our results slightly higher because they might have been more likely than average buyers when purchasing multiple items together during peak periods such as Christmas shopping season!

Takeaway:

Approaching the topic of hypothesis testing from a different angle, I’m going to talk about a null hypothesis and an alternative hypothesis. The null hypothesis is the statement that you assume to be true at the start of your experiment. The alternative hypothesis is the statement you assume to be false.

The process of determining which one is right involves making assumptions about populations and then comparing these with data collected during an experiment or study. If your results don’t match up with what they should be based on those assumptions, then we can conclude that there’s no difference between your population and theirs (null) or vice versa (alternative).

Conclusion

In conclusion, hypothesis testing is a method used to test the null hypothesis or alternative hypothesis. It can be used in many situations such as when you want to test your theory that two populations have equal variances (statistical hypothesis), whether there is a difference between any two groups (clinical hypothesis), or whether there are differences between various measurements of data from different sources (predictive hypothesis). The steps for performing an appropriate statistical test are as follows: 1) identify a statement about the population (called an empirical statement); 2) collect some data about that population; 3) calculate a statistic for each sample using this data; 4) compare these statistics against one another using some form of statistical procedure called analysis; and 5) conclude from these results if they support or refute our original statement about the population