3rd monthly

• Chapter III of a research paper is titled "Results & Discussion" and is divided into sub-sections describing each set of experiment or observations.
• The findings of a research study are presented in the "Results" section, which can be summarized in tables, pictures, or graphs.
• The "Analysis of Data" section interprets the findings and shows the significant features in the table, figures, or graphs.
• The "Discussion" section functions to interpret results in light of what was already known about the subject, explain new understanding of the problem after taking results into consideration, and explain how the results that tested the hypotheses under the study.
• The purpose of R&D is to process operations.
• Research data interpretation involves interpreting research results from which inferences are drawn, usually numerical results such as percentages, and correlation coefficients.
• Analysis in data interpretation involves categorizing, ordering, manipulating, and summarizing data to obtain answers to research questions.
• Interpretation in data interpretation involves making inferences pertinent to the research relations studied, and drawing conclusions about these relations.
• Data interpretation can be done through direct visual observations of raw data, after organizing the data in tables, after making graphical representations, after calculations using numerical/statistical methods, or after mathematical modelling.
• Data refers to raw, unprocessed facts and figures, consisting of individual pieces of information that are typically collected through various methods such as surveys, experiments, observations, or measurements.
• Information is derived from data when it is organized, structured, and presented in a meaningful way, involving the processing, analysis, and interpretation of data to extract relevant insights and patterns.
• Information provides context and understanding to the data.
• Facts are verified and validated information that is supported by evidence or proof, representing statements or assertions that are considered true.
• Knowledge is the understanding, awareness, or familiarity gained from facts, information, and experience, involving the integration and synthesis of various facts and information into a coherent framework.
• Knowledge represents a deeper level of understanding and can be applied to solve problems, make decisions, or create new insights.
• Data becomes information when it becomes relevant to a decision problem, and information becomes fact when the data can support it.
• Linear regression and logistic regression are common techniques used in experimental studies.
• Regression analysis explores the relationship between a dependent variable and one or more independent variables, determines the strength and direction of the relationship, and predicts the value of the dependent variable based on the values of the independent variables.
• Statistical data analysis is a set of methods used to collect, analyze, present, and interpret data.
• Descriptive statistics summarize and describe the main features of the data, including measures such as mean, median, mode, standard deviation, and range.
• It's important to note that failing to reject the null hypothesis does not confirm the null hypothesis as true.
• The decision-making process must be based on data, not on personal opinion or belief.
• Statistical methods play a crucial role in conducting experimental studies, providing a framework for analyzing and interpreting data, drawing meaningful conclusions, and making informed decisions.
• Facts are what the data reveals.
• Statistics assist in decision making under uncertainties, based on numerical and measurable scales.
• Inferential statistics allow researchers to make inferences and draw conclusions about a population based on a sample of data, involving hypothesis testing and confidence interval estimation.
• Inferential statistics help determine the statistical significance of findings and assess the likelihood that observed effects are not due to random chance.
• Descriptive statistics provide a snapshot of the data's central tendency, dispersion, and distribution.
• The level of significance (alpha) is typically set to control the probability of making a Type I error.
• Failing to reject the null hypothesis does not necessarily mean that the null hypothesis is true with absolute certainty.
• Hypothesis testing involves formulating a null hypothesis (H0) and an alternative hypothesis (Ha), and researchers collect data and use statistical tests, such as t-tests or analysis of variance (ANOVA), to determine whether there is enough evidence to reject the null hypothesis in favor of the alternative hypothesis.
• Interpretation opens new avenues of intellectual adventure and stimulates the quest for more knowledge.
• Select the proper statistical tool to make useful interpretation of your data.
• After collecting and analyzing the data, the researchers have to accomplish the task of drawing inferences followed by report writing.
• Errors can arise due to false generalization and/or due to wrong interpretation of statistical measures, such as the application of findings beyond the range of observations, identification of correlation with causation.
• Interpretation is a basic component of research.
• Through interpretation, the researcher can understand the abstract principle that works beneath the findings.
• Interpretation could extends beyond the data of the study to inch the results of other research, theory and hypotheses.
• Extraneous information, if collected during the study, must be considered while interpreting the final results of the research study, for it may prove to be a key factor in understanding the problem under consideration.
• Knowledge becomes useful when it is used in the successful completion of a decision process.