The federal government, through a series of historical precedents, has an obligation to assist states when an incident “causes damage of sufficient severity and magnitude to warrant major disaster assistance under the Stafford Act to supplement the efforts and available resources” (Sylves, 2015, p. 92) of already participating actors. However, the nature of presidential disaster declarations relies on loose delineations on its use, and many requests may be viewed as controversial and politically charged in choice. The fact that declarations are highly discretionary and subjective must not be forgotten, and it is important to consider each approval and denial of aid and its amount with a critical eye that not only takes in the human and economic loss of the disaster, but also the political forces behind each decision based on previous and future intergovernmental relations and status.

                Let’s say there is a hurricane poised to hit Florida and nearby states in two weeks’ time. The hurricane has been rated on the Saffir-Simpson Hurricane Scale as a category three hurricane with winds liable to hit up to 110 miles per hour, meaning possibly that “structures (may) sustain damage; manufactured housing (is) likely to be destroyed; trees (may) be blown down. Inland and coastal flooding” (Phillips, 2015, p. 13) are completely possible. In this instance, it may be wise for a gubernatorial request to be made before the hurricane hits Florida or the president may move forward and offer assistance even without a request or preliminary disaster assessment (PDA) (Sylves, 2015, p. 114). Requests carried out this way are beneficial as agencies in coordination with the Federal Emergency Management Agency (FEMA) can immediately dispatch anticipated resources needs, set up an Emergency Operations Center (EOC), and begin the process of fully utilizing and supporting emergency management systems up to the national level. However, there are limits on presidential disaster declarations due to the bureaucratic nature of its process. While PDAs can be provided for initial disaster and response needs, declarations appear to be far more concerned with immediate, short-term response and recovery operations rather than long-term recovery, which is where the bulk of assistance is needed to return communities to normalcy. Response must concern itself with strategic objectives rather than just the operational needs of the moment, and while the nation and national government may feel that they have reasonably met the needs of a disaster after the first few weeks, the people and communities need months or even years to progress and move forward personally, socially, and economically into an environment of normalcy and security.

                From what I have read, I think disaster declarations are a significant and necessary part of the measure of any functioning emergency management system, and that assistance should always be considered a bottom-up phenomenon. Yet, it is a pliable and often politically motivated set of actions that utilizes offering assistance as a strategy of “political responsiveness more than…on the basis of objective need” (Sylves, 2015, p. 121). Though there are issues with the system, it is always better to have more than enough resources, than to be lacking in response, as past generations of government have found.

References

Phillips, B. D. (2015). Disaster recovery (2nd ed.). Boca Raton, FL: Taylor & Francis Group.

Sylves, R. (2015). Disaster policy and politics: Emergency management and homeland security (2nd ed.). Washington, DC: CQ Press.

As we learn more about statistics, 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. Just because something statistically appears to be true, or have a connection, or predict the next President, it does not necessarily mean that it is more probable.
According to David Lane, a low probability value casts doubt on the null hypothesis. How low must the probability value be in order to conclude that the null hypothesis is false? Although there is clearly no right or wrong answer to this question, it is conventional to conclude the null hypothesis is false if the probability value is less than 0.05. More conservative researchers conclude the null hypothesis is false only if the probability value is less than 0.01. When a researcher concludes that the null hypothesis is false, the researcher is said to have rejected the null hypothesis.
When the null hypothesis is rejected, the effect is said to be statistically significant. This research paper scenario that we analyzed claims to have a meaningful contribution to the literature even though the research was exploratory in nature, and traditional levels of significance to reject the null hypotheses were relaxed to the .10 level. That level is entirely way too high for the null hypothesis to be false, and thus significant, therefore it is not that significant. It turns out that when the procedures for hypothesis testing were developed, something was ‘significant’ if it signified something. Thus, finding that an effect is statistically significant signifies that the effect is real and not due to chance. Higher probabilities provide less evidence that the null hypothesis is false.
The null hypothesis is essentially the ‘devil’s advocate’ position. That is, it assumes that whatever you are trying to prove did not happen, therefore the object is to reject it. This research scenario is not significant. This research was exploratory in nature, and exploratory research “intends merely to explore the research questions and does not intend to offer final and conclusive solutions to existing problems” (http://research-methodology.net). Exploratory research helps determine the best research design, data-collection method and selection of subjects. It should draw definitive conclusions only with extreme caution, hence this scenario needs to be extremely careful to make any claims considering it is exploratory in nature and the traditional levels of significance to reject the null hypothesis were relaxed to the .10 level.

Lane, David. Introduction to Statistics. Rice University; University of Houston.