Question
250 -300 words only with references
Dnp-830 Wk 5 DQ 1Respond to Darreis
A t-test is a type of inferential statistic used as a hypothesis testing tool which permits testing of an assumption related to a population or two data sets of a small population. The t-test sometimes called the dependent sample t-test, is used to see if there is a significant difference between the means in two unrelated groups. A Null hypothesis is used to test for significant differences (Maverick, 2018). The t-test takes a sample from each of the two groups and generates the problem statement by assuming a null hypothesis. The population means from the two unrelated groups are equal:[H0: u1 = u2 ]. Most often the alpha level (p) is set at 0.05 to allow for either rejecting or accepting the alternative hypothesis. The population means are notequal: HA: u1 ≠ u2] (Laerd statistics, (n.d.).
Assumptions for a t-test include the scale of measurement (scale of measurement applied to the data collected follows a continuous or ordinal scale). Another assumption is a simple random sample (data is collected from a representative, randomly selected portion of the total population), next assumption is data. Data, when plotted, results in a normal distribution, bell-shaped distribution curve. A reasonably large sample size is another assumption. The larger sample size necessitates the distribution of results should approach a normal bell-shaped curve. The final assumption is homogeneity of variance. Homogeneous, or equal, variance exists when the standard deviations of samples are approximately equal (Maverick, 2018). An alpha of p 0.05 is used as the cutoff for significance. If the p-value is less than 0.05, the null hypothesis is rejected, there is no difference between the means. This conclude that a significant difference does exist.
References
Laerd Statistics, (n.d.). Independent t-test for two samples, Retrieved from: https://statistics.laerd.com/statistical-guides/independent-t-test-statistical-guide.php
Maverick, J.B., (2018). What assumptions are made when conducting a t-test? Investodedia. Retrieved from: https://www.investopedia.com/ask/answers/073115/what-assumptions-are-made-when-conducting-ttest.aspv
Dnp-830 Wk 5 DQ 1Respond to Sharlisa
Thank you for your post. When you perform a hypothesis test in statistics, a p-value helps you determine the significance of your results. Hypothesis testing are employed to test the validity of a claim that is made about a population. This claim that you are testing, is called the null hypothesis. The alternative hypothesis is the one you would believe if the null hypothesis is concluded to be untrue (Dahiru, 2008). The evidence in the trial is your data and the statistics that go along with it. All hypothesis tests ultimately use a p-value to weigh the strength of the evidence (what the data are telling you about the population). The p-value is a number between 0 and 1.A small p-value (typically ≤ 0.05) indicates strong evidence against the null hypothesis, so you reject the null hypothesis. A large p-value (> 0.05) indicates weak evidence against the null hypothesis, so you fail to reject the null hypothesis. p-values very close to the cutoff (0.05) are considered to be marginal (could go either way) (Dahiru, 2008). Always report the p-value so others can draw their own conclusions.
Reference:
Dahiru, T. (2008). P - value, a true test of statistical significances? A cautionary note . Annals of Ibadan Postgraduate Medicine . Retrieved from https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4111019/
Substantive Post Yes | No
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Answer
Responses
Response 1
Hello Darreis, I agree with you that the t-test is an inferential statistic that tests the hypothesis thus permitting one to examine the assumptions related to the data sets. Indeed, the t-test is broadly used for small populations (Kim, 2015). Drawing from your discussion, it is evident that the ordinal and continuous scales as levels of measurements in t-tests promote the validity of the findings while the random sampling assumption reduces bias in the research (Emerson, 2015). The large sample assumption allows for the results distribution to approach the bell-curve shape. This assumption further reduces the error margin which improves the validity and reliability of the findings. To contribute to your discussion, the external validity of the findings which is explained as the application of the results in other populations is covered by the assumption of a large sample size. In addition, the application of p-value allows one to reject or accept a hypothesis. The p-value is vital in inferential tests as it demonstrates the significance in the variables’ relationship.
References
Emerson, R. W. (2015). Convenience sampling, random sampling, and snowball sampling: how does sampling affect the validity of research? Journal of Visual Impairment & Blindness, 109(2), 164-168.
Kim, T. K. (2015). T test as a parametric statistic. Korean journal of anesthesiology, 68(6), 540.
Response 2
Hello Sharlisa, let me first recognize the rich discussion on performing hypothesis test in statistics where the p-value plays a critical role. Indeed, the validity of the study is examined using the hypothesis testing where the claim for the given population is evaluated. Hypothesis are developed from the literature findings and relate to the study objectives and questions. Based on the p-value, the researcher can determine which hypothesis between null and alternative that should be believed. To contribute to your discussion, the application of the p-value transcends testing the hypothesis to include examining the relationship between variables (Wasserstein & Lazer, 2016). This is particularly in the correlation analysis in inferential statistics. In evaluating the relationship between variables from the collected data, it is critical to report the p-value where the significance is based on the number being less than 0.05 which allows one to reject the null hypothesis and allow the alternative hypothesis. Tools such as SPSS are used in this regard thus the researcher must demonstrate competence in their use.
Reference
Wasserstein, R. L., & Lazar, N. A. (2016). The ASA’s statement on p-values: context, process, and purpose. The American Statistician, 70(2), 129-133.
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