When the research sample consists of individual specific cases, researchers face unique challenges due to the limited size or specific nature of the cases involved. However, several general statistical methods can be applied to ensure the reliability and validity of the research and its results. These methods include descriptive statistics, which help summarize the sample’s characteristics using measures such as the mean and standard deviation; inferential statistics, which are used to draw predictions or inferences about the population based on the sample; regression analysis to understand the relationships between variables; and chi-square tests to examine relationships between categorical variables. Correlation analysis can also be used to measure the strength of relationships between variables. Furthermore, Bayesian methods provide a unique approach by leveraging prior knowledge and updating the analysis with new data. The importance of these methods in ensuring the accuracy of research lies in their ability to interpret data systematically, ensure reliability through hypothesis testing and confidence intervals, reduce bias that could affect the results, and allow researchers to draw valid, evidence-based conclusions. They also ensure the replicability and reproducibility of results, which enhances the credibility of the research. Overall, statistical methods remain essential tools that help researchers understand complex data and draw valid, scientifically supported conclusions.
You should specify what are the individual cases? In addition, the statistical method used depends on the sample size and the nature of the data distribution... A good question that needs to be discussed further.
Also what is the "research sample"? Is it the result of an individual observation or, for example, the one piece of a byological tissue?
The research sample is the vocabulary that the researcher chooses in a specific scientific way and on which the study is based.@
The varying perceptions of a statistician’s value, from “critical” to “not needed for 99.9% of applications”: A national study of human research ethics committees
"Currently, much health and medical research is wasted due to inappropriate study design or analysis. Study designs could be improved using an expert review from a qualified statistician. The ethical review process is an ideal stage for this input; however, we do not know how many ethics committees in Australia have access to a qualified statistician. To answer this question, we approached all human research ethics committees in Australia. Sixty percent of committees had access to a qualified statistician, either as a full committee member or as a non-member who could be consulted when needed, but this dropped to 35% after accounting for statistical qualifications. Many committees rely on “highly numerate” researchers in place of qualified statisticians, as they viewed research experience and advanced statistical training as equivalent. Committees without access to statisticians tended to locate responsibility for study design with other parties, including researchers, trial sponsors, and institutions. Some committee chairs viewed formal statistical input as essential to the work of the committee; however, there was also a common belief that statistical review was only applicable to some study designs, and that “simple” or “small” studies did not need review. We encountered a surprising variance in practice and attitudes towards the use of statisticians on research ethics committees. The current high number of research studies receiving approval without statistical review risks approving studies that will in the best-case waste resources and in the worst-case cause harms due to flawed evidence."
https://osf.io/preprints/osf/u423k_v1
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