Questions and Topics We can Help You To Answer
Respond to each of the following with a mimnum of 200 words each with a reference and totaling at least 400 words and 2 references.
Response 1
In its simplest definition, hypothesis testing is a technique used to draw conclusions (Evans, 2013). More specifically, hypothesis testing uses means, proportions, standard deviations, variances, et cetera to draw inferences between differing hypotheses (Evans, 2013).
Hypothesis testing begins by formulating the hypotheses to be tested. Once the hypotheses are selected, the level of significance must be tested. In other words, the acceptable margins of error must be chosen. This is the determination of how accurate the test must be. Next, the conditions under which a determination is made must be chosen. Evans labels this step as determining a decision rule (Evans, 2013). Next, data is collected and various statistical values are calculated. Finally, the decision rule is applied and conclusions of the testing are made.
When evaluating decision rules, an analysis of variance (ANOVA) may be used. In order to use ANOVA, outcome measures are random and independently obtained (Evans, 2013). Further, outcomes must be normally distributed with equal variances. When these conditions are met, the test is said to be parametric. However, if the assumptions are false or otherwise cannot be made, the test is said to be nonparametric.
Response 2
What is hypothesis testing? Explain the general process and the steps included in conducting a hypothesis test? What is the difference between parametric and nonparametric hypothesis testing
Hypothesis testing involves drawing inferences about two contrasting propositions (hypotheses) relating to the value of a population parameter, such as a mean, proportion, standard deviation, or variance. (Evans, 2013) In hypothesis data they like to use sample data to analyze and come up with the population and etc. It really would depend on what is being analyze.
Conducting a hypothesis test can be very challenging and informative. We have to first determine is the situation or even due to chance or is there a cause. Then, we have to examine the cause to prevent it from happening again. Then we need to streamline everything that happen from beginning to. In hypothesis testing there is a lot of methods that can be used; such as traditional and p value. P value is almost identical to the traditional. The beginning steps is exactly the same. This is what make the difference between right and wrong statistics.
Parametric information is more accessible. For example, if you are doing a story if it is a parametic hypothesis testing this means the information is there and you can review, but nonpararmetric no information will be available. Parametric is more in your face and up front whereas non parametric you have to work a little hard to get the resolution that you would like. Parametric is considered to be more powerful and acceptable than others.
Hypothesis testing is a way of coming up with a solution using data to get a
