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A branch of philosophy that says that complex things are built out of simple things. An important source of ideas for both science and Quantitative Research.

Normal Distribution

The most important type of distribution in Educational Research. When a Distribution is normal, then the Mean and Standard Deviation are related to each other in important systematic ways. It is also symmetrical, which indicates the Variable being measured is also systematic. Represented visually as the Normal Curve or sometimes the Bell Curve.

Null Hypothesis

The type of Hypothesis most often used in Inferential Statistics. When the Null Hypothesis is used, the researcher is actually testing the likelihood that the Null Hypothesis ought to be accepted. When that is shown to be highly unlikely, the actual Hypothesis is then accepted.



A standard of scientific research where the researcher seeks to avoid any personal bias from interfering with the research per se.


A systematic use of our senses, primarily visual, in research. Particularly important in Qualitative Research.

Online Learning Environment

An online learning environment refers to a virtual platform where students can access educational resources and interact with instructors and peers remotely.


In our scheme of understanding articles, the first few sentences or the first paragraph of the article.


A process found in Quantitative Research where the meanings of concepts are defined in terms of the operations used to measure them.

Oral History

A type of specialized series of Interviews where one or more informants are questioned in depth about their experiences and perceptions concerning an extended period of time.



The P value is defined as the probability under the assumption of no effect or no difference (null hypothesis), of obtaining a result equal to or more extreme than what was actually observed. The P stands for probability and measures how likely it is that any observed difference between groups is due to chance. 

  • Being a probability, P can take any value between 0 and 1 (0 ≤ p ≤ 1). 
  • Values close to 0 indicate that the observed difference is unlikely to be due to chance.
  • When P value is close to 1, it suggests no difference between the groups other than due to chance. 

Thus, it is common in medical journals to see adjectives such as “highly significant” or “very significant” after quoting the P value depending on how close to zero the value is.(Source)

The p-value is widely used in statistical hypothesis testing, specifically in null hypothesis significance testing. In this method, before conducting the study, one first chooses a model (the null hypothesis) and the alpha level α (most commonly 0.05). After analyzing the data, if the p-value is less than α, that is taken to mean that the observed data is sufficiently inconsistent with the null hypothesis for the null hypothesis to be rejected. However, that does not prove that the null hypothesis is false. The p-value does not, in itself, establish probabilities of hypotheses. Rather, it is a tool for deciding whether to reject the null hypothesis. (Source)

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