{"id":954,"date":"2024-03-23T06:32:47","date_gmt":"2024-03-23T06:32:47","guid":{"rendered":"https:\/\/eliteacademicessays.com\/free-essay-examples\/?p=954"},"modified":"2024-03-23T06:32:48","modified_gmt":"2024-03-23T06:32:48","slug":"analyzing-the-use-of-t-tests-anova","status":"publish","type":"post","link":"https:\/\/eliteacademicessays.com\/free-essay-examples\/analyzing-the-use-of-t-tests-anova\/","title":{"rendered":"Analyzing the use of t-tests, ANOVA"},"content":{"rendered":"\n<p>The&nbsp;research&nbsp;and evidence-based practice use statistics to determine if their prediction about a population is true.&nbsp;The first step of this process is to generate a hypothesis; a hypothesis is an inference made about a population.&nbsp;A hypothesis can either be null or alternative.&nbsp;A null hypothesis suggests that your prediction is caused by chance, but an alternative hypothesis suggests that your prediction is significantly true.&nbsp;Inferential statistics allows us to reach conclusions or tell if a conclusion is true by comparing it to the probability that the conclusion is due to chance.&nbsp;How does this work? Inferential tests are used to calculate a P-value, which is then compared to the probability that the results were caused by chance (Price, 2017).&nbsp;Some examples of inferential tests include chi-square tests, T-tests, ANOVA, Pearson Correlation, bi and multi-variate regression, and a one-sample test.<\/p>\n\n\n\n<p>Inferential tests check for correlation or causation, and an independent T-test and ANOVA measure causation.&nbsp;An independent T-test tests if there is a statistical mean difference between two samples or variables.&nbsp;Analysis of Variance (ANOVA), on the other hand, tests if there is a mean difference between two or more samples or variables.&nbsp;The difference between the two tests is that ANOVA can compare two or more treatments.&nbsp;However, ANOVA is more complicated than an independent t-test (Surbhi, 2017).<\/p>\n\n\n\n<p>The main advantage of inferential tests is that they can make inferences or draw conclusions about a population.&nbsp;The results of inferential tests can be generalized to the whole population when estimated to a specific confidence level.&nbsp;Therefore, a lot of time and effort is saved, which would have been used to test inferences on a population instead of a sample.&nbsp;On the other hand, a disadvantage of inferential tests is that a sample represents a population.&nbsp;Still, it is not the population itself, so we cannot be really sure about the results; so long as the results are to a certain level of confidence, a certain degree of uncertainty still exists.&nbsp;Furthermore, certain assumptions have to be met with inferential tests, and sometimes they are not, and that is a big problem.&nbsp;Lastly, the data is always vulnerable to biasness because the results always require people to make informed conclusions based on the theory they are testing.<\/p>\n\n\n\n<p>From the table on descriptive statistics, we can see an actual difference between the three&nbsp;variables.&nbsp;The study tests for mean life satisfaction score difference between people with no housing problems (MN= 12.71, SD=2.353), people with one housing&nbsp;problem&nbsp;(MN= 11.97, SD=2.588),&nbsp;and people with two or more housing&nbsp;problems&nbsp;(MN= 10.57, SD=2.594).&nbsp;According to the descriptive statistics, people with two or more housing problems have the lowest life satisfaction scores, and those with no housing problems have the highest satisfaction&nbsp;scores.&nbsp;To find out if this difference is significant ANOVA test was done.<\/p>\n\n\n\n<p>Our test seemed to have no problem with Homogeneity of variance (an assumption) since our p-value was more than our alpha value, which means our test was not significant p=0.122.&nbsp;However, the analysis of variance test showed that there was a significant difference in mean life satisfaction scores between our levels of material well-being F (2,934) =61.674, p=0.000.&nbsp;No housing problems seemed to significantly differ in life satisfaction scores with the other two variables (One Housing Problem p=0.001 and Two or More Housing p=0.000).&nbsp;However, its life satisfaction scores were greater than both variables.&nbsp;One housing problem had significantly lower life satisfaction scores than no housing problem p=0.01 but a significantly higher score than two or more housing problems p=0.00.&nbsp;Lastly, two or more housing problems had significantly lower life satisfaction scores than the other two variables (One Housing Problem p= 0.00 and No Housing Problem p= 0.00).<\/p>\n\n\n\n<p><strong>References<\/strong><\/p>\n\n\n\n<p>Price, J. C. (2017, August 21). <em>2.6 Analyzing the Data \u2013 Research Methods in Psychology<\/em>. Open Text. <a href=\"https:\/\/opentext.wsu.edu\/carriecuttler\/chapter\/analyzing-the-data\/\">https:\/\/opentext.wsu.edu\/carriecuttler\/chapter\/analyzing-the-data\/<\/a><\/p>\n\n\n\n<p>&nbsp;Surbhi, S. (2017, October 11). <em>Difference Between T-test and ANOVA (with Comparison Chart)<\/em>. Key Differences. <a href=\"https:\/\/keydifferences.com\/difference-between-t-test-and-anova.html\">https:\/\/keydifferences.com\/difference-between-t-test-and-anova.html<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>The&nbsp;research&nbsp;and evidence-based practice use statistics to determine if their prediction about a population is true.&nbsp;The first step of this process is to generate a hypothesis; a hypothesis is an inference made about a population.&nbsp;A hypothesis can either be null or alternative.&nbsp;A null hypothesis suggests that your prediction is caused by chance, but an alternative hypothesis [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[17],"tags":[],"class_list":["post-954","post","type-post","status-publish","format-standard","hentry","category-healthcare"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v22.8 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Analyzing the use of t-tests, ANOVA<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/eliteacademicessays.com\/free-essay-examples\/analyzing-the-use-of-t-tests-anova\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Analyzing the use of t-tests, ANOVA\" \/>\n<meta property=\"og:description\" content=\"The&nbsp;research&nbsp;and evidence-based practice use statistics to determine if their prediction about a population is true.&nbsp;The first step of this process is to generate a hypothesis; 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