Questions

1.    Inferential statistics refers to statistical techniques that make it possible to draw tentative conclusions (that is, makes inferences or predictions) about a population based on observations of a sample (Asadoorian & Kantarelis, 2005). The statistical techniques utilize probabilistic methods to analyze sample information from a given population so as to enhance our knowledge about the population.

Researchers use inferential statistics to draw conclusions or make predictions from sample data by using the measurements from the sample so as to reach a conclusion about a bigger, unmeasured population.

2. Probability is the possibility/fraction/chance/proportion that any specific outcome of an event will occur (Cozby & Bates, 2015).

Statistical significance relates to probability since it assists in determining the factors associated with the likelihood of occurrence of an event. If the probability is high, the statistical significance indicates if it took place due to any other factors or because that is just the way it happened.

3. In this study, the null hypothesis is the experimental group (that is yoga and medication), while the research hypothesis is the control group (that is, the support group and medication).

4. The most appropriate statistical test for the study would be chi-square since it would make a comparison of the observed data with the data expected according to the hypothesis. The role of probability in the research would be to determine whether the researcher’s hypothesis is correct based on the data collected.

5. True. A two-tailed test of significance is the most appropriate to use in this case since the investigator is checking for the possibility of the relationship in both directions.

6. The relationship between alpha level (or significance level) and Type I error is that there is a probability of making an error depends on the level that is set.  When doing a study, investigators choose the significant level (usually 0.005) depending on the consequences of making a Type I error.

Type II error is when the null hypothesis is accepted as true when in fact it is incorrect (that is the research hypothesis is correct).

The difference between Type I error and Type II error are that with Type I error the researcher makes a mistake of rejecting the null hypothesis when it is, in fact, true, whereas Type II error is when the researcher accepts the null hypothesis when it is, in fact, incorrect.

7. In the study the researcher is conducting a study with two independent variables, each comprising of two levels: Men and Female participants and high and low sugar diets (levels). The level of energy score is the dependent variable. Therefore, the researcher could use inferential statistics to evaluate the data. Inferential statistics are used by researchers to make inferences about the true difference in a population using outcomes of the sample data.

The most appropriate statistical test to use is the Chi-square since it distinguishes whether two or more observations on different populations depend on one another.

The criterion used is shown below.

H1: A relationship exists between sugar levels and sex levels

H0: A relationship does not exist between sugar levels and sex levels

8.

According to the scatter plot, the relationship between viewing time in seconds and the preference rating is linear, that is the viewing time increases with increasing aesthetic preference. It is very accurate to state that the longer the viewing times then, the greater the preference for paintings.

References

Asadoorian, M. O., & Kantarelis, D. (2005): Essentials of inferential statistics. University Press of America.

Cozby, P. C., & Bates, S. (2015): Methods in behavioral research. McGraw-Hill.

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