NUR 504 Week 6 Discussions 1

State in your own words what is meant by Type I and Type II errors. Why are these important? Name one thing that can be done to improve internal validity of a study.

ADDITIONAL INFO 

Type I and Type II errors

Introduction

In statistics, you’ll often hear two types of errors described as Type I and Type II. These terms refer to two different types of errors that can occur in statistical tests: one is when you reject a null hypothesis when it’s actually true; the other occurs when you fail to reject a null hypothesis that should be rejected.

A Type I error occurs when you reject a null hypothesis when it’s actually true.

A Type I error occurs when you reject a null hypothesis when it’s actually true. The null hypothesis is the statement that nothing has happened, whereas the alternative hypothesis is a statement of what did happen. For example:

• If two groups are compared and one group has more men than women, then there is no difference in how much time each group spends on their phones (the null hypothesis).

• If one group spends less time on their phone than another group does, then those who spend less time will be seen as more attractive by others (the alternative hypothesis).

A Type II error occurs when you fail to reject the null hypothesis when it’s actually false.

A Type II error occurs when you fail to reject the null hypothesis when it’s actually false.

This is often caused by a lack of statistical power, or the number of people in your sample who are likely to have different results than what you’re looking for (i.e., an alternative hypothesis).

What is a p-value?

A p-value is simply the probability of observing the sample statistic, or a more extreme value, if the null hypothesis is true. The smaller this number gets, the less likely it is that your sample data supports this hypothesis.

The p-value can be calculated using both binomial and Poisson distribution functions. Binomial distributions are used when you have two possible outcomes (such as heads or tails) and neither outcome occurs with any frequency in your population; for example:

“`js const binomialDistribution = new BinomialDistribution() const ppV = function(x){return Math.pow(1 – exp(-x), 2);};“`

P-values are represented by the Greek letter “p.”

P-values are represented by the Greek letter “p.” The p-value can range from 0 up to 1. A p-value less than 0.05 is considered a statistically significant result, while one greater than 0.05 is not statistically significant.

In statistics and probability theory, a hypothesis test is an investigation into whether some kind of relationship exists between two variables or groups of data—for example, whether gender influences job performance or if men are more likely than women to commit murder when they’re angry at their wives (the null hypothesis). If these relationships do exist in fact then we will find that our sample data supports our prediction while showing that it does not against the alternative hypothesis (e.g., “men are more likely”).

The p-value is the probability of observing the sample statistic, or a more extreme value, if the null hypothesis is true.

The p-value is the probability of observing the sample statistic, or a more extreme value, if the null hypothesis is true.

The smaller the p-value, the less likely it is that the null hypothesis is true.

There are two types of errors that can occur in statistical tests.

There are two types of errors that can occur in statistical tests. These are called Type I and Type II errors.

Type I error occurs when you make a conclusion based on an incorrect null hypothesis, or when you do not accurately reject the null hypothesis (the standard for rejecting a null hypothesis). If your test does not reject the null hypothesis, then you have failed to show that there is any difference between two populations or conditions. A good example would be testing whether two groups of people have similar differences in their scores on an assessment test. It might seem like it would be easy to know if they were different but sometimes there will be overlap between both groups so you cannot tell if anything has changed between them before taking into account other factors such as gender or age; therefore, we say that we did not adequately reject our null hypothesis due to these overlapping scores between both groups being equalized by chance alone.*

Conclusion

The purpose of this article was to demonstrate how statistical tests work and why they are so important. I also wanted to show that statistical testing can be a useful tool for understanding the world around us, as well as help us make better decisions when making choices about our lives.

Collepals.com Plagiarism Free Papers

Are you looking for custom essay writing service or even dissertation writing services? Just request for our write my paper service, and we'll match you with the best essay writer in your subject! With an exceptional team of professional academic experts in a wide range of subjects, we can guarantee you an unrivaled quality of custom-written papers.

Get ZERO PLAGIARISM, HUMAN WRITTEN ESSAYS

Why Hire Collepals.com writers to do your paper?

Quality- We are experienced and have access to ample research materials.

We write plagiarism Free Content

Confidential- We never share or sell your personal information to third parties.

Support-Chat with us today! We are always waiting to answer all your questions.