Data Results and Analysis Description
DataResults and Analysis Description
BasicRules for Understanding Results in a Research Study
Understandingresearch is a strategic aspect that requires rules and guidelines.First of all, one basic rule that assists in the understanding ofresearch is the methodology used has to be relevant to the samplestudy and leaving little or no room for bias. Secondly, the qualityof data has to be high enough to warrant reliance in the conclusionderived. The other aspect is the validity of data has to beascertained, no use of ‘cooked` data as it leads to the wrongconclusions. The other rule requires the sample size to be largeenough and from a randomized selection so that influence to biasedresults is mitigated (Houser, 2018).
ClinicalSignificance and Statistical Significance
Thequality of an aspect or data being important is known as‘significance`. When it comes to statistical significance, usesprobability in hypothesis testing (expressed as ‘p`). Thelikelihood of apparent differences of the results between a giventreatment and a set of control groups are determined to be eitherreal and not as a result of chance. For example, when a sample ofpeople in medical studies is carried out, the findings tend to beused in another population. However, the findings may be misleading,biased or there were people who gave wrong answers intentionally. Theuse of statistical significance is to address these concerns. Such anaspect as ‘bias` cannot be calculated through mathematic logic butuses probabilistic to test the likelihood of chance which is justtemporal and may not hold up in future (Houser, 2018). On the otherhand, Clinical Significance refers to the ability to apply clinicaljudgment in aspects that cannot be statistically determined. It isthe measurement of the magnitude of the differences in the effects oftreatment in relation to clinical practice.
Descriptivestatistics and inferential statistics in research
Theaspect of analytically showing data or make a summary of data in sucha manner that it gets some meaning, for example putting the data in acertain pattern that makes analysis easy, is referred to asDescriptive Statistics (Houser, 2018). In descriptive statistics, theanalysis of the data does not lead to the making of conclusions withrespect to the hypothesis being investigated but rather used to justdescribe the data and make it easier to analyze. It is used in thepresentation of raw data in a meaningful way, such as the use oftables or graphs so that the data being analyzed can be interpretedin a simpler manner. Therefore, Descriptive statistics is used tomeasure central tendency of data and also show how it is spread. Onthe other hand, when it comes to Inferential Statistics, show moredepth in data analysis. Such statistics as mean and standarddeviation tend to be calculated. Therefore, conclusions can be madefrom the sample group in relation to the inferences made afterpredictions from the data are made (Houser, 2018).
Houser,J. (2018).Nursing research: Reading, using and creating evidence (4thed.). Sudbury, MA: Jones & Bartlett.
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