If you study in computer science then on the first semester we’ll get a lecture about data on statistics. On this session, I will share my knowledge about basic statitics. Please give me feedbacks if you have questions or comments
Definition
Variables: Variables are things that we measure, control, or manipulate in research. They differ in many respects, most notably in the role they are given in our research and in the type of measures that can be applied to them.
Observational vs. experimental research. Most empirical research belongs clearly to one of those two general categories. In observational research we do not (or at least try not to) influence any variables but only measure them and look for relations (correlations) between some set of variables. In experimental research, we manipulate some variables and then measure the effects of this manipulation on other variables.
Dependent vs. independent variables. Independent variables are those that are manipulated whereas dependent variables are only measured or registered.
Variable Types
Variables differ in "how well" they can be measured. Measurement error involved in every measurement, which determines the "amount of information” obtained. Another factor is the variable’s "type of measurement scale."
Nominal variables allow for only qualitative classification. That is, they can be measured only in terms of whether the individual items belong to some distinctively different categories, but we cannot quantify or even rank order those categories. Typical examples of nominal variables are gender, race, color, city, etc.
Ordinal variables allow us to rank order the items we measure in terms of which has less and which has more of the quality represented by the variable, but still they do not allow us to say "how much more.” A typical example of an ordinal variable is the socioeconomic status of families.
Interval variables allow us not only to rank order the items that are measured, but also to quantify and compare the sizes of differences between them. For example, temperature, as measured in degrees Fahrenheit or Celsius, constitutes an interval scale.
Ratio variables are very similar to interval variables; in addition to all the properties of interval variables, they feature an identifiable absolute zero point, thus they allow for statements such as x is two times more than y. Typical examples of ratio scales are measures of time or space.
Accuracy vs. Precision
Accuracy: A measure of how close an experimental result is to the true value.
Precision: A measure of how exactly the result is determined. It is also a measure of how reproducible the result is.
Absolute precision: indicates the uncertainty in the same units as the observation
Relative precision: indicates the uncertainty in terms of a fraction of the value of the result
Parent vs. Sample Populations
Parent population: Hypothetical probability distribution if we were to make an infinite number of measurements of some variable or set of variables.
Sample population: Actual set of experimental observations or measurements of some variable or set of variables.