Statistical hypothesis testing

values are). These statistics are relevant to calculate because they allow for more meaningful calculations that allow us to draw more specific and accurate conclusions about claims such as the standard error, confidence intervals, z-scores and statistical significance. The bar chart in Figure 1 shows the frequency that each Quebecker chose their feelings about Canada. It shows that a majority of respondents falls at 50 or above with the most frequent value is 100. Table 1 shows a collection of

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In scientific inference, what we want to know is the probability that the null hypothesis (H0) is true given that we have obtained a set of data (D); that is, p(H0|D). What null hypothesis significance testing tells us is the probability of obtaining these data or more extreme data if the null hypothesis is true, p(D|H0). (p. 747) Kirk (1996) went on to explain that NHST was a trivial exercise because the null hypothesis is always false, and rejecting it is merely a matter of having enough power. In

the sample population, descriptive statistics, as well as the analysis of drug with the mean comparison, was done. The analysis involved the use of student t-test since a sample population of 30 was chosen. At 5% level of significance, the null hypothesis was then tested, and the overall results and discussion of the results were as presented below. Precisely, to test the effectiveness of the drug, the test was carried out at three levels: using descriptive statistics, using the z-score transformation

A contingency table is a table that has two rows that are crossed arranged by two columns. It is a suitable means for displaying data that could be categories by two unalike variables with each of them having only two likely results (Rosner, 2016). Additionally, a contingency table is basically a presentation arrangement that is used to evaluate and document the association among two or more categorical variables. Furthermore, it is the categorical equal to the scatterplot that is used to study the

What are three things that are dangerous/bad about using null hypothesis significance testing? Clearly explain why each of these is dangerous/bad. Be sure to cite which article(s) you got each of these 3 things from (15 points). What are ways to minimize these dangers? (10 points) Use of null hypothesis significance testing, while excellent in theory, has its own inherent limitations. Cohen (1994) suggests that the null hypothesis, does not give us the answer to questions we

Many Empirical Research involve statistical techniques to make reliable interpretation and inference of study results. P values are one of the important techniques used in statistical analysis. Researchers routinely consider a small p value (less than 0.05) to be "significant”, that is, to have a low probability of obtaining a result that is extreme under the null hypothesis. However, a small p value can also be misleading sometimes and can cause severe problems in the interpretation of study results

research methods that underpin psychology as a legitimate and reliable science. As an empirical science, psychology has a well-entrenched tradition of Null Hypothesis Significance Testing (NHST) (Fritz, Scherndl, & Kühberger, 2012). Despite its limitations, and extensive criticism, NHST remains the most commonly used method of statistical testing in psychology (Bakker & Wicherts, 2011; Fritz et al., 2012). In an attempt to create a consistent, replicable, and cumulative body of psychological research

The Lady Tasting Tea: Summary of the book The lady tasting tea takes the reader on a fascinating trip in the history of the statistical revolution. The contributions of many brilliant minds to the field of statistics as well as their personal lives and historical perspectives were described. The story began in a sunny afternoon in Cambridge in the 1920s. A group of scientists was having a tea party when a lady claimed that there was a difference in taste between the cups where tea was poured into

preventing getting the flu”. In this case, the problem being researched about is the flu the possible solutions to this problem which the use of vaccines is being analyzed. The null hypothesis of this experiment states that there is no effective vaccine for preventing getting the flu while the alternative hypothesis states that there is an effective vaccine for preventing getting the flu.