Advantages And Disadvantages Of Uniform Crime Report

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The Uniform Crime Reports (UCR) are official data on crime in the United States that are published by the Federal Bureau of Investigation (Biderman, A. D. & Reiss, A. J,1967). The UCR reports all types of crimes such as rape, robbery both with and without violence, manslaughter etc. The reports also gives details on the number of arrests and characteristics of the person arrested such as colour, age, race etc. One of the major disadvantages of UCR is that its participation is voluntary and some law enforcements agencies fail to supply data making it difficult for the report to have conclusive and reliable data. The data only reports crimes recorded by the police. Many crime victims do not report their experience to law enforcers for

a variety of reasons such as fear of reprisal, viewing the crime as a private matter and fear of stigmatization in the society e.g. in cases of rape. Thus, the data in the UCR is always an underestimate. In legal terms these unreported or undiscovered crimes are known as the dark figure of crime. The UCR only reports the most serious crime incidents such that when a person commits several crimes only the most serious one is reported. The UCR leaves out some important information as it only collects the most relevant data. Information such as weapons used in rape with force is not collected.
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Hypothesis tests are procedures employed to assist in making of rational decisions based on the reality of effects. Hypothesis testing tests a claim about a parameter in the population of concern using data taken from a sample. A hypothesis is tested by determining the probability that the sample statistic could be selected if the hypothesis about the population parameter is true(Ning- Zhong Shi, Jian Tao, 2008).
Hypothesis testing is usually done in four major steps.

We begin by stating the hypothesis. In stating the null hypothesis we state a value of the population that we consider to be true which is known as the null hypothesis. In hypothesis testing the presumption is that the claim we are testing is true. The decision is made by determining whether the assumption is true. The reason for testing the null hypothesis is because we think it could be wrong. We state what we believe is wrong about the null hypothesis in an alternate hypothesis (Ning- Zhong Shi, Jian Tao,2008) The alternative hypothesis contradicts the null hypothesis by stating that the real value of a population parameter is less than, greater than or unequal to the value stated in the null hypothesis. We then set the criteria for the decision, by stating the level of significance. This refers to the criteria upon which judgment is made. If the null hypothesis falls within the accepted level of significance then we accept the null hypothesis and reject the alternate. The third step is computing the test statistic that enables the researcher to determine the probability of obtaining sample outcomes if the null hypothesis is true. The test static is used to make the decision regarding the null hypothesis. The last step is making the decision. The value of the statistic guides on making the decision about the null hypothesis. Null hypothesis is accepted if the sample mean has a high probability of occurring when the null hypothesis is true. If the sample mean has a low probability of occurring when the null hypothesis is true, we reject the null

hypothesis.
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Human experimentation refers to the use of human beings as experimental subjects. In this type of experiment there are some inbred limits that results in unacceptable variations on the subjects. There are limits to how far, the researcher can expose the variations on humans that results in limited observation and information. The limits may be imposed as legal conditions by the state or federal government. For example where the variations have a high likelihood of causing permanent damage on an individual, the experiment cannot be performed, in spite of the benefits it may bring if the experiment turns out positive.
Humans may be traumatized by the experiments. An example is the Obedience experiments by Milgram’s (Milgram, 1963). Some of the subjects who were participants in the experiment were reported to have suffered from disconnection from reality as a result of the stress they were subjected to. Despite individuals being warned of the possible implications of the experiments and consenting to the experiment, sometimes they do not fully understand the depth of the possible implications to them and to their family members. When the results turn out negative, or go too far beyond the expected results, humans become traumatized. For example if individuals contract untreatable diseases, become deformed or die, them or the family members become traumatized.
Some human experiment may cause the development of new fears and anxieties. Some experiences may cause irrational fear from the participant due to the variables that they are exposed to. Some may lead to the human believing things that are not true such as the experiment by John Broadus Watson on Little Albert. The researcher through his experiment induced irrational fear of rats by little Albert.
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A Quasi experiment refers to a research study whereby treatment occurs naturally, hence lack the element of random control. This type of experiment is used to estimate the causal effect of an intervention on its target population. Under Quasi experiments, the researcher only controls treatment to the subjects in a non random manner (D.T, Stanley and J.C, 1966).A controlled experiment on the other hand, refers to a scientific experiment in which the researcher tests a hypothesis by observing changes caused by alterations to the independent variable. In a controlled experiment the subjects are placed into two groups namely the control group and the experimental group. The groups are exposed to similar variables apart from the factor being studied, so that the effect of that factor can be determined. The factor being tested is applied on the experimental group, and the impact is compared to the control group.
The main difference between a control and quasi experiments is that: while in quasi the researcher cannot randomly allocate participants to various conditions of the experiment, for a controlled experiment the observer can adjust the independent variable to determine the impact on the dependent variable. The quasi experiment is not a true experiment while the controlled is. For an experiment to be considered a true experiment it should be possible to randomly assign the conditions of an experiment on the participants.