What insight or ideas did you gain from learning each of these concepts

DQ 4 Mod 4

Reflect upon two concepts that you learned in this course.

· What are the concepts? What insight or ideas did you gain from learning each of these concepts? Were there aspects of the concepts that you would challenge?

· What is the importance of these concepts to public health? How will you use this new wisdom in your current or future career?

· Optional: Offer feedback on how the course and/or facilitation of the course can be improved.

Reflection is a mental process that challenges you to use critical thinking to examine the course information, analyze it carefully, make connections with previous knowledge and experience, and draw conclusions based on the resulting ideas. A well-cultivated critical thinker raises vital questions and problems, formulating them clearly and precisely; gathers and assesses relevant information, using abstract ideas to interpret it effectively; comes to well-reasoned conclusions and solutions, testing them against relevant criteria and standards; thinks open-mindedly within alternative systems of thought, recognizing and assessing, as need be, their assumptions, implications, and practical consequences; and communicates effectively with others in figuring out solutions to complex problems. (Paul & Elder, 2008)

In order to earn maximum credit, the comment should be more than your opinion, and more than a quick “off the top of your head” response. Be sure to support your statements, cite sources properly, cite within the text of your comments, and list your reference(s). The response must be a minimum of 250 words.

Paul, R. & Elder, L. (February 2008). The miniature guide to critical thinking concepts and tools. Foundation for Critical Thinking Press.


Already answered

DQ 1

Your comments will be graded on how well they meet the Discussion Requirements posted under “Before You Begin.”

We are constantly looking for information on the health of people and the country. According to one site, health statistics provide key indicators that help us know about the conditions of life in a country (Importance, n.d.). The author goes on to say these statistics help us understand the impact of health on people and work for their betterment. As we monitor the health of a population, we enhance our understanding of strategies to promote its health (Importance, n.d.).

Discuss whether or not you feel health planners, government officials, and healthcare organizations are using social issues/pressures or health statistics to determine strategies? Which is more reliable?


Importance. (n.d.). Retrieved from http://www.nlm.nih.gov/nichsr/usestats/importance.html

Uses. (n.d.). Retrieved from http://www.nlm.nih.gov/nichsr/usestats/uses.html


There are several ways to gather health data.  Health Statistics gives health planners, government officials, and health care organizations information to let them know about how people are living in their country (Importance, n.d.).  Health statistics can focus on certain individuals with the same condition, or a nation of people to get an idea on how their health is doing.  Using health statistics can give them past information as well as current information as a comparison to find a better way to live healthier for the future.  Using social issues/pressures limits the amount of data because the data is always changing.

I feel using health statistics is more reliable because the information is gathered from individuals by experiments and testing as well as observations and allows professionals to evaluate the results.  Health statistics can grasp social issues on society and can compare it to the data from the past.  This allows them to strategize better health care to the world.  If there is an epidemic that arises in a certain part of the world, health statistics can look at what kinds of health problems that country had as well as the current health conditions to measure the progress.  I do not feel social issues/pressures in capable of creating feasible health strategies.  Bottom line is health statistics can provide data that can allow healthcare organizations to adjust spending on equipment, public expenditures and determine the needs for care.  It goes beyond what social issues/pressures can provide for statistics.  Using health statistics, in my opinion can come up with better healthcare strategies for individuals as well as the world.  We need to ensure healthcare organizations are using their data that will improve strategy and ideas and not waste the data provided to them (Marklogic, 2017).




Importance. (n.d.). Retrieved from http://www.nlm.nih.gov/nichsr/usestats/importance.html

Uses. (n.d.). Retrieved from http://www.nlm.nih.gov/nichsr/usestats/uses.html



· Discussion

· Identify the use of frequency distribution and the differences between three types of skew/distribution curve and how they relate to the standard error.

Module Overview

As we learned in Module 1, data can be classified into various types. We now turn our attention to statistical techniques that Health Scientists use to analyze data. At this point we concern ourselves with descriptive statistics to examine a sample. Later in the course we will turn our attention to inferential statistics—those techniques used to make generalizations to a wider population (Dancey et al., 2012).


The frequency distribution is defined as a table or graph that is well organized to display one or more valuables (McGraw Hill Education, 2016). There are various uses of frequency distribution, and these include it shows whether the results are low or higher and if they are all concentrated in the same area or spread to the all over the whole scale. It helps the medical researchers by enabling them to view the entire data easily. Another use of frequency distribution is that it gives clear and summarized presentations of the value. The frequency distribution can be applied in healthcare to help medical researchers have a clear understanding of the patient or research data. This will help them to make a well-informed decision. Frequency distribution helps the researchers to understand the measurements.

The difference between normal, positive and negative skew is that normal curve is informed of a symmetric curve bell-shaped. The normal curve is well designed having the pick at the center and the curve ‘tails’ approaching the x-axis, but they never touch the axis. The normal curve is commonly applied for statistical inference (Australian Bureau of Statistics. 2013). These statistical inferences are commonly found in Bio-statics while positively skewed distribution is said to be a distribution that heaps up at the lower end and trails towards the upper end. The end of the distribution is on the positive scale. The mean of the distribution is greater than the median, and in the same distribution, the average is higher than the mode. Positive skew commonly occurs while the results are positive. For example, many children who were vaccinated against measles have shown a positive response. Negative distribution is different in that the distribution tracks towards the lower end. In this distribution, the tail of the distribution is on the positive scale. For example, when children are vaccinated against measles some of them will show negative results.






Australian Bureau of Statistics. (2013). Statistical language: Measures of central tendency. Retrieved from  http://www.abs.gov.au/websitedbs/a3121120.nsf/home/statistical+language+-+measures+of+central+tendency

McGraw Hill Education. (2016). Chapter 2: Describing data: Frequency distributions and graphic presentation. Retrieved from  http://highered.mcgraw-hill.com/sites/dl/free/0070880441/40846/Chapter2.pdf


Stattrek. (2016). What is normal distribution? Accessed from  http://stattrek.com/probability-distributions/normal.aspx



DQ 3


· Discussion

· Identify Ho and H1 in a scientific journal article.

· Explain significance and p-value in an article.

Module Overview

In this module, we shift gears from descriptive statistics to inferential statistics. Inferential statistics are used to determine the probability that a conclusion based on analysis of data from a sample is true (Norman & Streiner, 2008). As statisticians, we keep in mind that when gathering data on a sample of people there is a possibility for random error. In other words, measurements drawn at random from a population of individuals of interest will differ by some amount as a result of random processes.

We start by formulating a null hypothesis. A null hypothesis is an assumption that there is no significant difference between a sample mean and a population mean. We then formulate an alternate hypothesis that is mutually exclusive.

The primary goal of a statistical test is to determine whether an observed data set is sufficiently different from what we would expect under the null hypothesis that we should reject the null hypothesis.


Statistical inferences bases on hypothesis tasting to draw conclusion about a particular population using a sample population. Tasting of the hypothesis enhance the strength of the evidences inferred from the sample population in relation to the entire population (Cook et al., 2004). Accepting or rejecting a hypothesis will depend on the statistical p-values obtained. The investigator formulates a null (Ho) and alternative hypothesis (H1). This are concise truth statements about the existing relationships between the predictors and the outcomes of the population.

In the study where the development of the Major Depression was compared in the different life stages in children with comparison to the environment predisposition of the victims was analyzed. The null hypothesis (Ho) was that there is no putative relationship amongst the environmental stressors in the early life ages on the causative of Major Depression (MD) in the adult life of the victims. This meant that the occurrence of MD in the patient’s life in the adult age did not correlate with the predisposition of the victim to stressful life and psychopathological strain in the young age. The alternative hypothesis (H1), therefore was that psychopathological torture and stress predisposing of children correlate with the occurrence of the MD.

To taste for the Ho, the data analyzed should prove the correlation between the stressor (Early Parent Loss (EPL) in this case and the occurrence of the psychiatric disorders like MD, schizophrenia (SCZ) and Bipolar Depression (BPD) (Agid et al., 1999). This means that the differences between the occurrences of the psychiatric disorders in children that were not predisposed to stressors and those predisposed to EPL and parental separation should be statistically significant with a p-value lower than 0.05 or 0.01 in two-tailed or one-tailed t-tastes. When the calculated a p-value is lower than the statistical a p-value, the Ho is rejected and the H1 is accepted with a conclusion that there is a statistical difference between the occurrence of the psychiatric illness in children predisposed to EPL and parental separation than in health children that are not exposed to such stressors (Statistics Learning Centre, 2011).

The p-value, is used in medical research to determine whether the test of the hypothesis on the sample population was statistically significant or it was non-significant. Statistical significance does means that there is truth about the H1 (Johnson, 2008). This means that the differences in the population means ad the sample means is not by chance but by factors interplay. The P-value is expressed as “P =0.05” or “P = 0.01”, implying two-tailed and one-tailed testes. In this study, the p-valueP = 0.001, OR=3.8, inferred that EPL during childhood significantly amplified the chances of a person developing MD in the adulthood. In the parental separation, P=0.008, implying that parental separation at 9 years strikingly increased the chances of developing MD.

The high rate in EPL significantly increased schizophrenia patients (OR = 3.8, P = 0.01) before age 9 years (OR = 4.3, P = 0.01). In the control studies, individuals that experienced EPL stress reported lower income, divorced frequently, were lonely, smoked, and were physical ill (P = 0.03–0.001). In this instance, the p-value inferred that the occurrences of lower income, divorced frequently, were lonely, smoked, and were physical illness in adults that suffered EPL in their childhood was not by chance by a fact of predisposing on the environmental stressors (Cook et al.., 2004).


Agid, O. B. J. M. B. H. T. M. U. B., Shapira, B., Zislin, J., Ritsner, M., Hanin, B., Murad, H., … & Lerer, B. (1999). Environment and vulnerability to major psychiatric illness: a case control study of early parental loss in major depression, bipolar disorder and schizophrenia. Molecular psychiatry4(2), 163.

Cook, A., Netuveli, G., & Sheikh, A. (2004). Chapter 4: Statistical inference. In Basic skills in statistics: A guide for healthcare professionals (pp. 40-52). London, GBR: Class Publishing. eISBN: 9781859591291.

Johnson, L. (2008). Principles of hypothesis testing for public health. National Center for Complementary and Alternative Medicine. Retrieved from  https://ippcr.nihtraining.com/handouts/2011/Hypothesis_2011.pdf

Statistics Learning Centre. (2011, December 5). Hypothesis tests, p-value – Statistics help

. Retrieved from  http://www.youtube.com/watch?v=0zZYBALbZgg

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