can be characterized best as an objective description of a phenomenon
that is void of any conclusions. They tend to detail the action and the
environment of which said phenomenon is comprised of. It provides information
that can lead to the conclusion or to a hypothesis in the context of the
Sample – in a scientific context, it can simply
be considered to be a representative part of a greater whole. Samples are a
small group of things that are part of a larger group of things. Their contents
are usually unified by one or more similar characteristics.
Mean – in the context of experimental data, the
mean is the numerical average of the data set. It can have any categorical
context assigned to it. It is mean to help describe the data by setting a point
of reference that helps to assess the general trends of data.
Normal Distribution – a probabilistic phenomenon
that seems to be quite common when considering various natural and experimental
phenomenon. Characterized by the “bell-curve”, this helps us understand the
probability of an outcome with reference to the mean as well as several other
Research – a way to question and answer said
question in a methodic fashion. We use research as a way to figure out how the
natural world and theoretical constructs work and don’t work.
Experiment – a systematic set of steps that
follow a rigid protocol, which assist in assessing the potential causality
between two events. There is almost always a dependent and an independent
variable, various conditions may apply, and
Hypothesis – a statement based on a question
that asserts how a specific phenomenon will play out. It implies causality and
zeros in 2 variables, whilst still considering, subliminally, the possibility
of confounding variables.
Variable – an event that plays a role in the
causal link between itself and another variable in the context of an
experiment. Usually has a singular categorical context and can either be
independent, dependent, extraneous, or confounding, depending on its relation
to the experiment. Independent variables are manipulated, dependent variables
are what change accordingly, and confounding variables are what can influence
the relationship between the two.
Correlation – the data-driven relationship between the
dependent and independent variable in the context of either an experiment or an
observation. Can be positive, negative, or nonexistent, and it should never be
confused with causation.
Results’ – results that fell within the bounds of the probabilistic paradigm of
a given study. These results cannot be attributed to just chance, and can be
considered to have some sort of ‘significance’. They help to describe the true
relationship between variables since they can’t be explained by chance and thus
are evidently linked by some potentially causal phenomenon.