Scenarios 1

Statistical significance is found in a study, but the eff, in reality, ty is very small (i.e., there was a very minor difference in attitude between men and women). Were the results meaningful? An independent sample t-test was conducted to determine whether differences exist between men and women on cultural competency scores. The samples consisted of 663 women and 650 men taken from a convenience sample of public, private, and non-profit organizations. Each participant was administered an instrument that measured his or her current levels of cultural competency. The © 2016 Laureate Education, Inc. Page 2 of 2 cultural competency score ranges from 0 to 10, with higher scores indicating higher levels of cultural competency. The descriptive statistics indicate women have higher levels of cultural competency (M = 9.2, SD = 3.2) than men (M = 8.9, SD = 2.1). The results were significant t (1311) = 2.0, p

For this scenario you must do the following:

· Critically evaluate the sample size.

· Critically evaluate the statements for meaningfulness.

· Critically evaluate the statements for statistical significance.

· Based on your evaluation, provide an explanation of the implications for social change.

Scenarios 2

A study has results that seem fine, but there is no clear association to social change. What is missing? A correlation test was conducted to determine whether a relationship exists between level of income and job satisfaction. The sample consisted of 432 employees equally represented across public, private, and non-profit sectors. The results of the test demonstrate a strong positive correlation between the two variables, r =.87, p < .01, showing that as level of income increases, job satisfaction increases as well.

For this scenario you must do the following:

· Critically evaluate the sample size.

· Critically evaluate the statements for meaningfulness.

· Critically evaluate the statements for statistical significance.

· Based on your evaluation, provide an explanation of the implications for social change. Meaningfulness in Statistics Assignment

ADDITIONAL INFORMATION 

Meaningfulness in Statistics

Introduction

I often hear people say, “Significance is the proof that something is important.” This is not true.

In statistics, ‘significance’ does not mean ‘importance’. A ‘significant’ relationship may be of no importance. You cannot judge the significance of a result if you do not know its variability.

In statistics, ‘significance’ does not mean ‘importance’.

When you read a survey report, or see an article in the news about a new study on happiness, it’s easy to get caught up in the idea that these results are somehow important. But this isn’t the case—the word “significance” has no objective meaning. It does not mean “importance” or “worthiness.”

The word “significance” comes from Latin and means “to mark out.” In statistics, it refers to how strongly two variables are related: if one variable increases as another decreases then this is considered significant (i.e., strong). However, there are many ways in which significance can be calculated: some methods use confidence intervals around each estimate; others take into account multiple comparisons within your data set; yet others look at correlations between two variables instead of just one (for example).

A ‘significant’ relationship may be of no importance.

A significant result may be of no practical importance.

This can happen if the research question was poorly framed, or if there was a bias in the sample. In this case, you should report your results with a warning that they do not support a conclusion about any causal relationship. You should also make sure to explain why this might be so (e.g., “The data were collected from only one site”).

A significant result may be due to chance alone – even though it doesn’t seem likely based on previous experience with similar data sets; for example, in an experiment where participants are asked whether they would buy something at different prices and then their answers are compared across different pricing levels (e.g., $5 vs $10). If we had repeated these experiments many times but never found any effect—or even just one time—it would be difficult to conclude anything about whether people really wanted those products more than others!

You cannot judge the significance of a result if you do not know its variability.

You cannot judge the significance of a result if you do not know its variability.

Variability is one of three main factors that determine how significant a result is. The other two factors are sample size and effect size. The more variability, the less likely that a result will be statistically significant (i.e., have p-value < 0). This can be seen in Figure 1 below:

The significance of a result is not an objective feature or property of that result.

The significance of a result is not an objective feature or property of that result.

Saying that “the data show x” does not mean that there was a relationship between the data and x, nor does it mean that if we changed the data so as to make them say something else, then they would say something different. Instead, significance is simply one person’s subjective judgment about how likely it is that some specific relationships are true given their observed behavior.

Conclusion

Statistics is a powerful tool. It can be used to make predictions, estimate risk and analyze data. But when you use it, remember that statistics is an art and not a science. Statistics cannot answer all your questions. The best conclusions come from carefully observed evidence and accurate interpretation of results in context with other evidence whose relevance has been verified by experts in the field (e.g., research studies).