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.

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.