The Test That Will Be Uses

So, we should reject the null hypothesis H0. At a 0.05 level of significance level, we conclude that there is a significant difference between the average height for females and the average height for the males.

4) Discuss the implications of changing the level of significance to a larger value. What mistakes or error could increase if the level of significance in

Cohen’s paper The Earth is Round (p>0.05) is a critique of null-hypothesis significance testing (NHST). In his article, Cohen presents his arguments about what is wrong with NHST and suggests ways in which researchers can improve their research, as well as the way they report their research. Cohen’s main point is that researchers who use NHST often misinterpret the meaning of p-values and what can be concluded from them (Cohen, 1994). Cohen also shows that the NHST is close to worthless. NHST is a way to show how unlikely a result would be if the null hypothesis were true. A Type I error is where the researcher incorrectly rejects a true null hypothesis and a Type II error is where the researcher incorrectly accepts the false null

Because the p-value of .035 is less than the significance level of .05, I will reject the null hypothesis at 5% level.

Testing allows the p-value that represents the probability showing that results are unlikely to occur by chance. A p-value of 5% or lower is statistically significant. The p value helps in minimizing Type I or Type II errors in the dataset that can often occur when the p value is more than the significance level. The p value can help in stopping the positive and negative correlation between the dataset to reject the null hypothesis and to determine if there is statistical significance in the hypothesis. Understanding the p value is very important in helping researchers to determine the significance of the effect of their experiment and variables for other researchers

The Testing trilogy by Joelle Charbonneau is a thrilling story about who to trust in a world that is unpredictable. The series consists of the books The Testing, Independent Study, and Graduation day, all of which follow the main character Malencia Vale, or Cia for short. The main point I found throughout these three is being careful about who you can trust. Because of this I chose a moralist type of criticism. It has a lot to do with how the characters are feeling even though it is the society that is corrupt so I found that this fit. I thought while reading these books that most of it was very well written and that the world building was spectacular. Though there is a romance portion that isn't done very well until the third book. Also I have seen argued that the characters don't seem very realistic emotion wise. I feel that the story was very well done but that the emotions didn’t seem realistic in every character that you came across.

Given the following information, would your decision be to reject or fail to reject the null hypothesis? Setting the level of significance at .05 for decision making, provide an explanation for your conclusion.

An alpha level of 0.05 is arbitrary and was set as a standard by scientists. One of the key concepts in hypothesis testing is that of significance level or, the alpha level, which specifies the probability level for the evidence to be an unreasonable estimate. Unreasonable means that the estimate should not have taken its particular value unless some non-chance factor(s) had operated to alter the nature of the sample such that it was no longer representative of the population of interest. (Price, 2000)

Information about statistical significance and confidence interval is presented and reviewed. There was good use of tables and figures that included titles and headings that were clearly and appropriately labeled. The results were also clearly displayed in tables with identifiable titles and labeled headings. The study included descriptive statistics. The study described the main characteristics in the dataset. The mean and standard deviation for each blood pressure measurement was calculated before and after crossing of the legs was performed by the study subjects. Inferential statistics were also present in this study. In order to test mean differences with three or more groups, an analysis of variance (ANOVA) statistical test is used. This research study conducted a repeated-measure ANOVA, which is when there are three or more measures of the same dependent variable

P-value represents a decimal between 1.0 to below .01. Unfortunately, the level of commonly accepted p-value is 0.05. The level of frequency of P>0.05 means that there is one in twenty chance that the whole study is just accidental. In other words, that there is one in twenty chance that a result may be positive in spite of having no actual relationship. This value is an estimate of the probability that the result has occurred by statistical accident. Thus, a small value of P represents a high level of statistical significance and vice

With a P-value of 0.00, we have a strong level of significance. No additional information is needed to ensure that the data given is accurate.

The outcome then depends on the data and information collected. While testing a hypothesis, the researcher might commit type 1 or type 2 error because either the null hypothesis or alternative hypothesis is rejected when it is true. Rejecting of the null hypothesis will result in type 1 error; and alternative hypothesis in type 2 error.  It is therefore important to allow room for possibility of committing either of these errors as no study is perfect (Chang, 2011).

Harvard professor Chris Argyris promoted the concepts of espoused theories of action and theories-of-use. Espoused theories of actions reflect what people say governs their behavior, while theories-of-use reflects how they actually behave.

A Type I error is the worst type of error a researcher can make while conducting an experiment. A Type I error is the rejection of the null hypothesis when the null hypothesis is actually true. Therefore, a Type I error is a false positive and it indicates that results are significant when they are not. For example, if a researcher states that the amount of antidepressants taken by individuals will decrease the number of suicides, the outcome could be potentially harmful. Individuals would become reliant on the medication and have expectations of the antidepressant lowering their likelihood of committing suicide. Unfortunately, this Type I error could potentially increase suicide. Setting a low alpha level will allow the researcher to avoid

The p-value is a measure of the strength of the evidence against the null hypothesis. The p-value is the probability of getting the observed value of the test statistic, or a value with the even greater evidence against Ho, if the null hypothesis is actually true. The smaller the p-value, the greater the evidence against the null hypothesis. If we have a given significance level, then we reject. If we do not have a given significance level, then it is not as cut-and-dried. If the P-value is less than (or equal to) α, then the null hypothesis is rejected in favor of the alternative hypothesis. And, if the P-value is greater than α, then the null hypothesis is not rejected. All statistical tests produce a p-value and this is equal to the probability of obtaining the observed difference, or one more extreme, if the null hypothesis is true. To put it another way if the null hypothesis is true, the p- value is the probability of obtaining a difference at least as large as that observed due to sampling variation. Consequently, if the p-value is small the data support the alternative hypothesis. If the p-value is large the data support the null hypothesis. But how small is ‘small’ and how large is large ‘?! Conventionally a p-value of 0.05 is generally regarded as sufficiently small to reject the null hypothesis. If the p-value is larger than 0.05 we fail to reject the null hypothesis. The 5% value is called the significance level of the test. Other significance levels that are