# 1- Classmate discussion The alpha value of 0.10 is significantly higher than any of the “customary” values for alpha: 0.05, 0.01, or 0.001 (Frankfort-Nachmias & Leon-Guerrero, 2015, p. 274), which is a cause to revisit the analysis. The risk of rejecting the null hypothesis is 10%, which means that high-stakes conclusions should not be adopted. However, in business management, 10% may be acceptable. It is important to consider what the implications for the study are. If the results may influence human well-being, for example, this would not be acceptable. On the other hand, if the results affect only low-cost factors, then higher risk is amenable. The context of this statement is important. Relative “meaningfulness” (Laureate Education, 2016) is derivable from that context and the “alpha” (Frankfort-Nachmias & Leon-Guerrero, 2015) value reflects the confidence and importance of achieving a reliable conclusion. The ‘relaxed’ levels of significance should probably be included within the findings, which would enhance the value of the study. Research that is inconclusive or that has low-level statistical significance may still be valuable. In providing the data and making inferences, at 10% rather than the standard 5% (or lower), data transparency may provide future researchers with usable information. Finally, the footnote provides evidence that the researcher is making ontological assumptions about the relationships observed in the study. In some instances, researchers have altered their data to achieve lower p-values (American Statistical Association, 2016), which fall into generally accepted ranges, and the admission in the footnote leads me to believe the data is accurately reported. For the purpose of encouraging future research and rendering data that can later be compared and contrasted, perhaps the writer has made a good decision. It may even occur, following this ‘exploratory’ stage, that 10% becomes a research norm; science is not always as precise as a researcher or practitioner may assume. Without additional information, it is impossible to arrive at a meaningful conclusion. References American Statistical Association. (2016). American Statistical Association releases statement on statistical significance and p-values. doi:10.1080/00031305.2016.1154108#.Vt2XIOaE2MN Frankfort-Nachmias, C., & Leon-Guerrero, A. (2015). Social statistics for a diverse society (7th ed.). Thousand Oaks, CA: Sage Publications. Laureate Education (Producer). (2016). Meaningfulness vs. statistical significance [Video file]. Baltimore, MD: Author 2- Classmate discussion Statistical Significance and Meaningfulness In response to the discussion for week-5 topic statistical significance and meaningfulness which is very importance because of the statistical importance and meaningfulness. Statistics which is a set of procedure which is commonly used by the social scientists to organize, summarize and communicate information. In general discussion Statistical significance does not speak to the probability that the null hypothesis or an alternative hypothesis is true or false, to the probability that a result would be replicated, or to treatment effects, nor is it a valid indicator of the magnitude or the importance of a result. The key to hypothesis tests is deciding if the observed difference (of some measure) relative to the underlying variance in the data is reasonable or not. If reasonable you do not reject the null hypothesis, but if it is not reasonable, you reject the null hypothesis in favor of the alternative (i.e. research hypothesis). Whereas statistical hypothesis is a procedure which allows us to evaluate hypotheses about population parameters based on simple statistics. The persistence of statistical significance testing is due to many subtle factors. A test of statistical significance addresses the question. How likely is a result, assuming the null hypotheses to be true? Randomness, a central assumption underlying commonly used tests of statistical significance, is rarely attained, and the effects of its absence rarely acknowledged. Although relationships are significant, they can be very minute relationships, very small differences, or very weak correlations. For example, researchers according to American Statistical Association (2016), have been obsessed to obtain a low and acceptable p-value to the extent that researchers may have weaken their data. Although statistical hypothesis testing does help you evaluate claims, it is important to understand the limitations of statistical significance and to interpret the results within the context of the research and its pragmatic, “real world” application. Journal editors are not to blame, but as publishing gatekeepers they could diminish its dysfunctional use. Once you start to understand how exciting the world of statistics can be, it is tempting to fall into the trap of chasing statistical significance. That is, you may be tempted always to look for relationships that are statistically significant and believe they are valuable solely because of their significance. As a scholar-practitioner, it is important for you to understand that just because a hypothesis test indicates a relationship exists between an intervention and an outcome, there is a difference between groups, or there is a correlation between two constructs, it does not always provide a default measure for its importance. Although relationships are significant, they can be very minute relationships, very small differences, or very weak correlations. In the end, we need to ask whether the relationships or differences observed are large enough that we should make some practical change in policy or practice. In the end, we need to ask whether the relationships or differences observed are large enough that we should make some practical change in policy or practice. References American Statistical Association. (2016). American Statistical Association releases statement on statistical significance and p- values. doi:10.1080/00031305.2016.1154108#.Vt2XIOaE2MN Baker, M. (2016). Statisticians issue warning over misuse of P values. Nature. Retrieved from www.nature.com Laureate Education (Producer). (2016). Meaningfulness vs. statistical significance [Video file]. Baltimore, MD: Author. Leon-Guerrero, A., & Frankfort-Nachmias, C. (2014). Essentials of social statistics for a diverse society. London: Sage Publications.

**1- Classmate discussion**

The alpha value of 0.10 is significantly higher than any of the “customary” values for alpha: 0.05, 0.01, or 0.001 (Frankfort-Nachmias & Leon-Guerrero, 2015, p. 274), which is a cause to revisit the analysis. The risk of rejecting the null hypothesis is 10%, which means that high-stakes conclusions should not be adopted. However, in business management, 10% may be acceptable. It is important to consider what the implications for the study are. If the results may influence human well-being, for example, this would not be acceptable. On the other hand, if the results affect only low-cost factors, then higher risk is amenable.

The context of this statement is important. Relative “meaningfulness” (Laureate Education, 2016) is derivable from that context and the “alpha” (Frankfort-Nachmias & Leon-Guerrero, 2015) value reflects the confidence and importance of achieving a reliable conclusion. The ‘relaxed’ levels of significance should probably be included within the findings, which would enhance the value of the study. Research that is inconclusive or that has low-level statistical significance may still be valuable. In providing the data and making inferences, at 10% rather than the standard 5% (or lower), data transparency may provide future researchers with usable information.

Finally, the footnote provides evidence that the researcher is making ontological assumptions about the relationships observed in the study. In some instances, researchers have altered their data to achieve lower p-values (American Statistical Association, 2016), which fall into generally accepted ranges, and the admission in the footnote leads me to believe the data is accurately reported. For the purpose of encouraging future research and rendering data that can later be compared and contrasted, perhaps the writer has made a good decision. It may even occur, following this ‘exploratory’ stage, that 10% becomes a research norm; science is not always as precise as a researcher or practitioner may assume. Without additional information, it is impossible to arrive at a meaningful conclusion.

References

American Statistical Association. (2016). American Statistical Association releases statement on

statistical significance and p-values. doi:10.1080/00031305.2016.1154108#.Vt2XIOaE2MN

Frankfort-Nachmias, C., & Leon-Guerrero, A. (2015). *Social statistics for a diverse society* (7^{th}

ed.). Thousand Oaks, CA: Sage Publications.

Laureate Education (Producer). (2016). *Meaningfulness vs. statistical significance* [Video file].

Baltimore, MD: Author

**2- Classmate discussion**

Statistical Significance and Meaningfulness

In response to the discussion for week-5 topic statistical significance and meaningfulness which is very importance because of the statistical importance and meaningfulness. Statistics which is a set of procedure which is commonly used by the social scientists to organize, summarize and communicate information.

In general discussion Statistical significance does not speak to the probability that the null hypothesis or an alternative hypothesis is true or false, to the probability that a result would be replicated, or to treatment effects, nor is it a valid indicator of the magnitude or the importance of a result. The key to hypothesis tests is deciding if the observed difference (of some measure) relative to the underlying variance in the data is reasonable or not. If reasonable you do not reject the null hypothesis, but if it is not reasonable, you reject the null hypothesis in favor of the alternative (i.e. research hypothesis). Whereas statistical hypothesis is a procedure which allows us to evaluate hypotheses about population parameters based on simple statistics.

The persistence of statistical significance testing is due to many subtle factors. A test of statistical significance addresses the question. How likely is a result, assuming the null hypotheses to be true? Randomness, a central assumption underlying commonly used tests of statistical significance, is rarely attained, and the effects of its absence rarely acknowledged. Although relationships are significant, they can be very minute relationships, very small differences, or very weak correlations. For example, researchers according to American Statistical Association (2016), have been obsessed to obtain a low and acceptable p-value to the extent that researchers may have weaken their data.

Although statistical hypothesis testing does help you evaluate claims, it is important to understand the limitations of statistical significance and to interpret the results within the context of the research and its pragmatic, “real world” application. Journal editors are not to blame, but as publishing gatekeepers they could diminish its dysfunctional use. Once you start to understand how exciting the world of statistics can be, it is tempting to fall into the trap of chasing statistical significance. That is, you may be tempted always to look for relationships that are statistically significant and believe they are valuable solely because of their significance.

As a scholar-practitioner, it is important for you to understand that just because a hypothesis test indicates a relationship exists between an intervention and an outcome, there is a difference between groups, or there is a correlation between two constructs, it does not always provide a default measure for its importance. Although relationships are significant, they can be very minute relationships, very small differences, or very weak correlations. In the end, we need to ask whether the relationships or differences observed are large enough that we should make some practical change in policy or practice. In the end, we need to ask whether the relationships or differences observed are large enough that we should make some practical change in policy or practice.

**References**

American Statistical Association. (2016). American Statistical Association releases statement on

statistical significance and p-

values. doi:10.1080/00031305.2016.1154108#.Vt2XIOaE2MN

Baker, M. (2016). Statisticians issue warning over misuse of P values. Nature. Retrieved from

www.nature.com

Laureate Education (Producer). (2016). Meaningfulness vs. statistical significance [Video file].

Baltimore, MD: Author.

Leon-Guerrero, A., & Frankfort-Nachmias, C. (2014). Essentials of social statistics for a diverse

society. London: Sage Publications.