Introductory Statistics: Conducting a Survey to Support a Research - Essay Example

Statistics Statistics Strategic Communications refers to the use of al or corporate communications to form, preserve or strengthen opinions that are favorable to the realization of the core goals of then institution among key audiences. Data becomes an important facet of Strategic Communications given the goals of the process. The general aim of the Strategic Communications is to promote favorable public policy outcome. It also reduces costs of doing business. Additionally, it supports marketing and operational effectiveness of the institution. Planning for an effective Strategic Communications requires that the firm has all the facts and data about its operations at hand (Mann & Lacke, 2010). Reducing the costs of operation requires that the institution has a record of its expenses and productions against incomes over a period of time. It is only possible to change the trend for the better of the company if statistics that support such a cause are available. Additionally, supporting market and operational effectiveness requires that the company has statistics about the market population and tastes and preferences in addition to their reactions to various marketing strategies. With data available over different facets of the firm that an organization intends to change, it is able to define strategic and actionable plans for its operations. The firm gets into a position of designing an implementation approach and plan that guides communicators in designing, preparing ad executing the strategic communications (Mann & Lacke, 2010). With effective Strategic Communication Planning and proper data, an institution gets into a position of creating a master plan or event specific plan to mitigate its public image. Sufficient data places a firm in a position to have support of its key stakeholders and decision makers and enables the plan to be implemented within the culture and resources of the organization. 2. In statistics, a population refers to the set of entities under study. For instance, the average height of School of Communication students is a population. In essence, this is hypothetical population as it includes the heights of those who have left school, those who are in session and students who will join the institution. It may be impossible to study all measure or survey the whole population since not all members are accessible. This includes the students who may join the school in future and those who have left. In this example, the population is ‘students’ and the parameter of interest is ‘height’. Arriving at the parameter of interest for the population involves surveying a subset of population that is referred to as a sample. Given some conditions, the sample is used to draw some inferences about the entire population. Therefore, population refers to the set of entities under study as sample refers to a subset of population that is used to draw certain inferences of the entire population under some set of conditions Thus, it is possible to draw inferences about a population by surveying a sample. The sample of population provides statistics and the inference is often prone to inaccuracies (Mann & Lacke, 2010). The uncertainties and inaccuracies are apparent due to fewer numbers of members in the sample than in the population. The process commonly used of obtaining samples called Simple Random Sampling often leads to inaccuracies as it assumes that each member of the population has equal chances of being included in the sample. Whether it is SRS or Cluster Sampling or Stratified Sampling that is used, the using a large sample is the most dependable way of improving accuracy of inference and ensuring that samples represent the population (Mann & Lacke, 2010). 3 Statistical Significance refers to the probability that an occurrence is not due to chance alone. The measure is an integral part of statistical hypothesis testing and is used as a crucial value of judgment. In statistics, the outcome of a research result is considered to be significant by more than its meaningfulness and importance. It has to be predicted as unlikely to have surfaced by chance alone. Tests for statistical significance are useful in determining the probability that the variables are actually unrelated in the population. The tests also become useful in filtering out unpromising hypotheses. These tests for statistical significance are overly used because they constitute a general yardstick that are comprehensible to many people and communicate crucial information about research projects which can be compared to other research projects. While using statistical significance measures, it is important for one to note than Statistical Significance does not assure researchers that the research had been carefully executed and designed (Mann & Lacke, 2010). The tests can be misleading as they are precise numbers that have no relationship to the practical significance to the outcomes of the research. This semester, I have set out to use statistical approach in changing my social ways of life. I will take a record of the events and places I visit and attend in a bid to reduce what I consider socially unacceptable behavior. I will use statistical significance to keep track of the changes that I desire and discern if the changes in behavior come from within or probability of not going to the unacceptable social events are by chance. 4 Hypothesis refers to the proposed explanation for a phenomenon. For scientific hypotheses, there is a requirement that there be a mode of testing the scientific method. Researchers base research hypotheses on prior observations that lack satisfactory explanations and that cannot be explained using existing theories. Whereas people confuse and use theory and hypothesis interchangeably, they bear different meanings. Theory refers to that which has undergone extensive research and is generally accepted as explanation for a particular observation (Mann & Lacke, 2010). A hypothesis, on the other hand, has not undergone any research and is usually lined up for thorough and rigorous research to test its accuracy. A working hypothesis refers to the provisionally accepted hypothesis for further research. When a similar relation or correlation between different phenomena is investigated, the hypothesis for the research cannot be examined I the same way a researcher would examine non-co relational hypotheses such as examining the effects of proposed new law on nature. In a case that requires no correlation, cases in which the tested remedy shows no impacts do not falsify the hypotheses. Statistical tests are used to determine the effects observed in the event that no real relation as hypothesized existed. The research assumes the existence of a relation In the event that the likelihood is sufficiently small (for instance less than 1%) (Mann & Lacke, 2010). Otherwise, any other observed effects may be due to pure chance. Area under the normal curve is one of the statistical tools used to test hypotheses. The normal curve is an inestimable number of credible probability models known as a family of distributions. Every member of the family is portrayed by setting the strictures (m and ) of the distribution to particular rates (Mann & Lacke, 2010). The members of the family are alike in that they share the same outline, are proportioned, and have overall area underneath of 1.00 (Mann & Lacke, 2010). They vary in where the intermediate of the distribution falls, given by , and in the unpredictability of scores around the midpoint, given by . The region between any two scores and the scores that cut off a given region on any particular normal distribution can be found. Specification of a research question is a methodological point of departure in scholarly research in both social and natural sciences. The research answers the posed question. Specifying the research question is the first methodological step the investigator takes while undertaking a research. The research question serves two principle purposes. Foremost, it determines what kind of research and where the researcher will conduct his research. It also identifies the specific objectives of the survey. The region of rejection refers to the range of values that leads a researcher to reject a null hypothesis. In order to test hypothesis, the researcher collects data and computes test statistics from the sample data (Mann & Lacke, 2010). In the event that the statistics fall within a specific range, the investigator rejects the hypothesis as null. The concept of region of rejection stems from here. Reference Mann, P. S., & Lacke, C. J. (2010). Introductory statistics. Hoboken, NJ: John Wiley & Sons. Read More