Exploring Inferential Statistics and Their Discontents

From the above output, we can see that the p-value is 0.000186, which is smaller than 0.05 (if we select a 0.05 significance level).

Since in a normal distribution, the curve is symmetrical, skewness can affect the accuracy to which normal distribution can be applied to a data set. To determine how close the distribution of the weight of the AFL population is to being normal, the degree of skewness was found as a large degree of skewness would reduce the practicality of applying normal distribution to the data set.

 Inferential statistics helps us to analyze predictions, inferences, or samples about a specific population from the observations that they make. “With inferential statistics, you are trying to reach conclusions that extend beyond the immediate data alone” (Trochim, 2006). The goal for this type of data is to review the sample data to be able to infer what the test group may think. It does this by making judgment of the chance that a difference that is observed between the groups is indeed one that can be counted on that could have otherwise happened by coincidence. In order to help solve the issue of generalization, tests of significance are used. For example, a chi-square test or T-test provides a person with the probability that the analysis’ sample results may or may not represent the respective population. In other words, the tests of significance provides us the likelihood of how the analysis results might have happened by chance in a scenario that a relationship may not exist between the variables in regards to the population that is being studied.

Because the p-value of .035 is less than the significance level of .05, I will reject the null hypothesis at 5% level.

Research results tell us information about data that has been collected. Within the data results, the author states the results are statistically significant, meaning that there is a relationship within either a positive and negative correlation. The M (Mean) of the data tells the average value of the results. The (SD) Standard Deviation is the variability of a set of data around the mean value in a distribution (Rosnow & Rosenthal, 2013).

The null hypothesis is rejected since the p-value is below the significance level of 0.05.

Researchers routinely choose an ◊-level of 0.05 for testing their hypotheses. What are some experiments for which you might want a lower ◊-level (e.g., 0.01)? What are some situations in which you might accept a higher level (e.g., 0.1)?

The information in the table below refers to the 2008 model year product line of BMW automobiles. Identify the Individuals, variables, and data corresponding to the variables in the table below. Determine whether each variable is qualitative, continuous, or, discrete. Please refer to problems #51 and #53 on page 13 for examples.

P-value represents a decimal between 1.0 to below .01. Unfortunately, the level of commonly accepted p-value is 0.05. The level of frequency of P>0.05 means that there is one in twenty chance that the whole study is just accidental. In other words, that there is one in twenty chance that a result may be positive in spite of having no actual relationship. This value is an estimate of the probability that the result has occurred by statistical accident. Thus, a small value of P represents a high level of statistical significance and vice

I rejected the null hypothesis and found out the p-value is smaller than the significance level. The p-value is greater than the significance level 0.05.

Visualize continuous variables, in this case, hemoglobin, with histograms before and after transformation to see if the data become more normal and if a mathematical transformation is required to turn the data in normally distributed variable.

The second step is defining the significance level, determining the degrees of freedom and finding the critical value. The a-level shows that for a result to be statistically significant, it cannot occur more than the a-level percentage of time by chance. The critical value can be obtained by using the t-test table. The degrees of freedom is

Since 3.27 the t statistic is in the rejection area to the right of =1.701, the level of

We created a survey using the Qualtrics program to test our hypothesis of whether political knowledge and political engagement translate to youth turnout in post-secondary students. The survey consisted of 36 close-ended questions consisting of multiple choice, dichotomous, likert scaling and semantic scaling options. The survey topics included 1) personal information 2) voting history 3) knowledge of voting information 4) political engagement and activism, and 5) testing political knowledge. We decided to ask close-ended questions because it allowed for easier data collection for quantitative data and we had already collected more rich

After executing two-step method of data transformation (Templeton, 2011), the researcher attained probable means and medians for both CPI (1.00) and SPI (1.05). Likewise, the Skewness figures comply with logically normalizing rules, in which the plausible range of normal and near-normal distribution is from -1 to 1. Conforming to histogram and normal Q-Q plots above, the researcher successfully reconstructed CPI and SPI data and prepared for the further inferential statistics examination. Descriptive statistics for CPI and SPI after transforming to normal and near-normal distribution are as in Table 5.7.