From a single cell to a baby in 9 months a developmental process that represents an amazing integration of increasingly complex phenomena. The study of these phenomena is called embryology, and the fi eld includes investigations of the molecular, cellular, and structural factors contributing to the formation of an organism.These studies are important because they provide knowledge essential for creating health care strategies for better reproductive outcomes. Thus, our increasingly better understanding of embryology has resulted in new techniques for prenatal diagnoses and treatments, therapeutic procedures to circumvent problems with infertility, and mechanisms to prevent birth defects, the leading cause of infant mortality. These improvements in prenatal and reproductive health care are signifi cant not only for their contributions to improved birth outcomes but also for their long-term effects postnatally. In fact, both our cognitive capacity and our behavioral characteristics are affected by our prenatal experiences,and factors such as maternal smoking, nutrition,stress, diabetes, etc., play a role in our postnatal health. Furthermore, these experiences, in combination with molecular and cellular factors, determine our potential to develop certain adult diseases,such as cancer and cardiovascular disease. Thus, our prenatal development produces many ramifi cations affecting our health for both the short and long term, making the study of embryology and fetal development an important topic for all health care professionals. Also, with the exception of a fewspecialties, most physicians and health care workers will have an opportunity to interact with women
of childbearing age, creating the potential for these providers to have a major impact on the outcome of these developmental processes and their sequelae.
In medicine and psychology, clinical significance is the practical importance of a treatment effect – whether it has a real genuine, palpable, noticeable effect on daily life.
Statistical significance is used in hypothesis testing, whereby the null hypothesis (that there is no relationship between variables) is tested. A level of significance is selected (most commonly alpha = 0.05 or 0.01), which signifies the probability of incorrectly rejecting a true null hypothesis. If there is a significant difference between two groups at alpha = 0.05, it means that there is only a 5% probability of obtaining the observed results under the assumption that the difference is entirely due to chance (i.e., the null hypothesis is true); it gives no indication of the magnitude or clinical importance of the difference.When statistically significant results are achieved, they favor rejection of the null hypothesis, but they do not prove that the null hypothesis is false. Likewise, non-significant results do not prove that the null hypothesis is true; they also give no evidence of the truth or falsity of the hypothesis the researcher has generated. Statistical significance relates only to the compatibility between observed data and what would be expected under the assumption that the null hypothesis is true.
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