Before any study can be undertaken, the researcher must determine what exactly will constitute the study’s target population, sample frame, and sample (if any of these terms is unfamiliar to you, I strongly encourage you to revisit the discussion of sampling as this information provides the foundation necessary to complete this case assignment). Also to be considered how he will go about recruiting subjects into his study.
Your Task for this Assignment
Discuss the following as it pertains to your study in 3-4 pages; incorporate this discussion into a 12-15 page final paper consisting of all prior SLP components.
A sampling plan for your study to address the research question your chose in Module 1. Be sure to outline clearly your study’s- target population sampling frame sample A recruitment strategy for enlisting participation prospective research subjects in your study
Hypothesis Testing An experimenter starts with a hypothesis about a population parameter called the null hypothesis. Data are then collected and the viability of the null hypothesis is determined in light of the data. If the data are very different from what would be expected under the assumption that the null hypothesis is true, then the null hypothesis is rejected. If the data are not greatly at variance with what would be expected under the assumption that the null hypothesis is true, then the null hypothesis is not rejected. Click here to read more about hypothesis testing.
The Central Limit Theorem
The Central Limit Theorem is a statement about the characteristics of the sampling distribution of means of random samples from a given population. That is, it describes the characteristics of the distribution of values we would obtain if we were able to draw an infinite number of random samples of a given size from a given population and we calculated the mean of each sample.
The Central Limit Theorem consists of three statements:
 The mean of the sampling distribution of means is equal to the mean of the population from which the samples were drawn.
 The variance of the sampling distribution of means is equal to the variance of the population from which the samples were drawn divided by the size of the samples.
 If the original population is distributed normally (i.e. it is bell shaped), the sampling distribution of means will also be normal. If the original population is not normally distributed, the sampling distribution of means will increasingly approximate a normal distribution as sample size increases. (i.e. when increasingly large samples are drawn)
The p-value is the probability of observing a test statistic that is as extreme or more extreme than currently observed, assuming that the null hypothesis is true. Click here for more on the p-value.
Describing Univariate (One Variable) Data; Univariate Statistics