Mth 231: Life Sciences Sampling And Populations
Life Sciences Sampling and Populations Paper
MTH/231
Life Sciences Sampling and Populations Paper
The core of biostatistics consists of the definition of a population and sampling, as they are the indicators of the fundamental concepts that are essential to understanding the statistics of the life and health sciences. The idea that a sample is illustrative of a given population, since a sample is derived from a specific, yet larger pool of information seems factually representative. Random sampling aides research in that it applies experimental design to the selection process and is the fairest means of sample collection, providing equal chance to the members of a given population being signified.
Populations
Populations, as defined by Triola and Triola (2006) are “a complete collection of all elements (scores, people, measurements and so on) to be studied. The collection is complete in the sense that it includes all the subjects to be …show more content…
studied. To serve as an example, all Kansan 9th graders will functionally be considered for a population. The data collected from the population are referred to as parameters and therefore are descriptive. The subjects or observations within the population are labeled with an N, or in the case of the example, figuratively, would be N=6000. The first function in this type of data collection is to have identified the target population, which has been done already.
Sampling
Sampling is one of the most essential notions of biostatistics, as it is the process of attaining useable information from greater groups of data called populations. “A sample is obtained from a larger population because in most instances, especially in the medical field, it is impossible to study the entire population” (Overholser & Sowinski, 2007, p. 629). The example to illustrate a sample will be the learning disabilities of the population of a classroom of Kansan 9th graders.
Comparing Samples and Populations
The basis for a sample comes from a population, but not inversely considered in the opposite regard. Generally, population is a vague term that encompasses people, animals, events, and etcetera. Anything that can be counted is considered a unit of population, however, if useful information cannot be extracted from its measure, it is considered not suitable for further research. Sampling moreover is concerned with a collection of a subset of individuals from a statistical populace to estimate the characteristics of the entire population. The example of the learning disabilities from the Kansan 9th graders may be representative of the instances applied primarily to more rural areas, since population density must be considered and is not necessarily representative of more populated areas.
To increase the chance that a sample is indicative of the population it is to represent is if the research is conducted without bias, convenience sampling, under coverage of the chosen target population, and sample source selection.
Starting research without bias needs to be a golden rule to follow, as sometimes the researcher may have a personal investment in the cause behind the research being conducted. Bias should not impair the researcher’s ability to effectively carryout the experiment. How the target population of interest is selected is just as important as the contact method chosen for the collection of data. In the digital age, it may be more beneficial for both the researcher and the researched to use the internet or telephone. Since that contact method is almost instantaneous, it eliminates the need to wait on such things as transit time in the mail or requiring physical travel. The Kansan 9th graders once again would be more representative of less populated areas than its more urban
counterparts.
Random Sampling
In such instances as random sampling from the population of interest, this technique is used to ensure each member of that population has an identical chance of being selected. The process of random sampling is done in a single step as each subject is selected independently of other member of the population. Only those Kansan 9th graders with learning disabilities would then be considered. A notable point about the simplicity of random sampling is the affluence of the assembly of the sample.
Conclusion
Overall, the core structure of biostatistics rests upon the understanding of population and sampling. A sample becomes representative of the target population provided that good research is conducted to maximize efficiency and the data that is collected is deemed useful enough for further study. “Biostatistics has established itself as one of the pillars on which biomedical research rests” (Afifi & Yu, 2010, p. 366). Furthermore, random sampling provides an equal opportunity of members of the population being selected for study, making it the fairest means possible.
References
Afifi, A. A., & Yu, F. (2010, March). Current research in biostatistics. American Journal of Ophthalmology, 149(3), 364-366. doi:http://dx.doi.org/10.1016/j.ajo.2009.11.023
Overholser, B. R., & Sowinski, K. M. (2007, December). Biostatistics primer: part 1. Nutritional Clinical Practice, 22(6), 629-635. Retrieved from http://ncp.sagepub.com/content/22/6/629
Triola, M. M., & Triola, M. F. (2006). Biostatistics for the biological and health sciences. Boston, MA: Addison Wesley/Pearson. Retrieved from University of Phoenix MTH/231 Class Materials Student Website.
Weiss, R. E. (2010, February). Bayesian methods for data analysis. American Journal of Ophthalmology, 149(2), 187-188. doi:http://dx.doi.org/10.1016/j.ajo.2009.11.011.
MTH/231
Life Sciences Sampling and Populations Paper
The core of biostatistics consists of the definition of a population and sampling, as they are the indicators of the fundamental concepts that are essential to understanding the statistics of the life and health sciences. The idea that a sample is illustrative of a given population, since a sample is derived from a specific, yet larger pool of information seems factually representative. Random sampling aides research in that it applies experimental design to the selection process and is the fairest means of sample collection, providing equal chance to the members of a given population being signified.
Populations
Populations, as defined by Triola and Triola (2006) are “a complete collection of all elements (scores, people, measurements and so on) to be studied. The collection is complete in the sense that it includes all the subjects to be …show more content…
studied. To serve as an example, all Kansan 9th graders will functionally be considered for a population. The data collected from the population are referred to as parameters and therefore are descriptive. The subjects or observations within the population are labeled with an N, or in the case of the example, figuratively, would be N=6000. The first function in this type of data collection is to have identified the target population, which has been done already.
Sampling
Sampling is one of the most essential notions of biostatistics, as it is the process of attaining useable information from greater groups of data called populations. “A sample is obtained from a larger population because in most instances, especially in the medical field, it is impossible to study the entire population” (Overholser & Sowinski, 2007, p. 629). The example to illustrate a sample will be the learning disabilities of the population of a classroom of Kansan 9th graders.
Comparing Samples and Populations
The basis for a sample comes from a population, but not inversely considered in the opposite regard. Generally, population is a vague term that encompasses people, animals, events, and etcetera. Anything that can be counted is considered a unit of population, however, if useful information cannot be extracted from its measure, it is considered not suitable for further research. Sampling moreover is concerned with a collection of a subset of individuals from a statistical populace to estimate the characteristics of the entire population. The example of the learning disabilities from the Kansan 9th graders may be representative of the instances applied primarily to more rural areas, since population density must be considered and is not necessarily representative of more populated areas.
To increase the chance that a sample is indicative of the population it is to represent is if the research is conducted without bias, convenience sampling, under coverage of the chosen target population, and sample source selection.
Starting research without bias needs to be a golden rule to follow, as sometimes the researcher may have a personal investment in the cause behind the research being conducted. Bias should not impair the researcher’s ability to effectively carryout the experiment. How the target population of interest is selected is just as important as the contact method chosen for the collection of data. In the digital age, it may be more beneficial for both the researcher and the researched to use the internet or telephone. Since that contact method is almost instantaneous, it eliminates the need to wait on such things as transit time in the mail or requiring physical travel. The Kansan 9th graders once again would be more representative of less populated areas than its more urban
counterparts.
Random Sampling
In such instances as random sampling from the population of interest, this technique is used to ensure each member of that population has an identical chance of being selected. The process of random sampling is done in a single step as each subject is selected independently of other member of the population. Only those Kansan 9th graders with learning disabilities would then be considered. A notable point about the simplicity of random sampling is the affluence of the assembly of the sample.
Conclusion
Overall, the core structure of biostatistics rests upon the understanding of population and sampling. A sample becomes representative of the target population provided that good research is conducted to maximize efficiency and the data that is collected is deemed useful enough for further study. “Biostatistics has established itself as one of the pillars on which biomedical research rests” (Afifi & Yu, 2010, p. 366). Furthermore, random sampling provides an equal opportunity of members of the population being selected for study, making it the fairest means possible.
References
Afifi, A. A., & Yu, F. (2010, March). Current research in biostatistics. American Journal of Ophthalmology, 149(3), 364-366. doi:http://dx.doi.org/10.1016/j.ajo.2009.11.023
Overholser, B. R., & Sowinski, K. M. (2007, December). Biostatistics primer: part 1. Nutritional Clinical Practice, 22(6), 629-635. Retrieved from http://ncp.sagepub.com/content/22/6/629
Triola, M. M., & Triola, M. F. (2006). Biostatistics for the biological and health sciences. Boston, MA: Addison Wesley/Pearson. Retrieved from University of Phoenix MTH/231 Class Materials Student Website.
Weiss, R. E. (2010, February). Bayesian methods for data analysis. American Journal of Ophthalmology, 149(2), 187-188. doi:http://dx.doi.org/10.1016/j.ajo.2009.11.011.