Statistical Inference As a Statistical Technique

Introduction

In the field of statistics, statistical inference denotes the method of using statistical techniques to obtain conclusions from groups of statistics, which come up from methods that are affected by indiscriminate variation. Examples of such sources of variation include observational inaccuracies and random sampling among others. Preliminary basics of such a system of processes for inferential as well as inductions are that the method must generate rational responses when applied to definite conditions and that it must be broad as much as needed to be applied transversely on a range of conditions.

Research Hypothesis for the Study

The hypothesis for the study done by McIlvain, Noland, and Bicke (2011), is inferred. The hypothesis is inferred because it is based on the view that caffeine use among the young generation has increased over the last couple of years. McIlvain, Noland, and Bicke (2011), tested their inferred hypothesis by determining the quantity of the stimulant consumed by a group of students from a particular university. McIlvain, Noland, and Bicke (2011), also tested the hypothesis by evaluating the attitudes about stimulant consumption, the reported supposed advantages as well as disadvantages of consuming the stimulant. They also assessed the motives for taking the stimulant as well as predictors of the stimulant use among the university students (McIlvain, Noland, & Bicke, 2011).

Dependent Variable and Independent Variable(s)

The independent variables for this study were age, gender, ethnicity and level of education (freshmen). The dependent variables in this study were symptoms of the stimulant (caffeine) intoxication and withdrawal, Father’s social index namely; attentiveness, staying awake and waking up, and predictors of the stimulant use (McIlvain, Noland, & Bicke, 2011). The other dependent variables included the milligrams of the stimulant (caffeine) consumed in a day, milligrams of the stimulant consumed by a student for every kilo of body mass in a day. Taking part in prearranged school activities was another dependent variable identified in the study.

The Method for Selecting Participants

The method used to select the participant of the study conducted was random sampling (Stephens, 2006). Three hundred freshmen students were randomly sampled from a population of 14,000 students attending Marshall University (McIlvain, Noland, & Bicke, 2011).

Types of Bias

Response bias might have arisen in this study because the survey tool (questionnaire) was self-administered. The student may have discussed the questionnaire, and as a result, they might have written down the answers they thought were the best suited to the questionnaire questions and the answers that resembled their fellow student’s responses (Monette, Sullivan, & DeJong, 2011). Sampling bias could have happened because McIlvain, Noland, and Bicke (2011), sampled only freshmen students from a single university.

Statistical Testing Procedure Used to Analyze the Results of the Study

Descriptive statistical analyses were obtained such as frequencies, percentages, means and standard deviations. Other statistical analyses included regression analysis, hypothesis testing using t-test, and correlational analysis using Principal Component Analysis (McIlvain, Noland, & Bicke, 2011).

Ethical Concerns

Consent to take part in the study was not sought from the students rather it was sought from the institutions only.

Study Concerns

The results of the study cannot be generalized to the public because, the study involved students from only one university (McIlvain, Noland, & Bicke, 2011). The study involved freshmen students only hence, the result cannot be generalized to the entire Marshall University student body. The study was not broad because it only involved a few classes within the university (McIlvain, Noland, & Bicke, 2011).

Conclusion

The study produced valid results. However, in future, the investigators should consider controlling the bias identified in the study and should broaden the study for results generalization.

References

1. McIlvain, G. E., Noland, M. P., & Bicke, R. (2011). Caffeine Consumption Patterns and Beliefs of College Freshmen. American Journal of Health Education, 42 (4), 235-244.
2. Monette, D. R., Sullivan, T. J., & DeJong, C. R. (2011). Applied Social Research: A Tool for the Human Services. Australia; Belmont, CA: Brookscole.
3. Stephens, L. J. (2006). Schaum’s outline of theory and problems of beginning statistics. New York: McGraw-Hill.