Statistics are used for purposes of description and we can also use it to make inferences.
Data analysis is the process of systematically applying statistical and/or logical techniques to describe and illustrate, condense and recap, and evaluate data.
As researchers, we diagnose the problem, specify statistical tests, retrieve then interpret analysis results.
Psychological statistics is the application of formulas, theorems, numbers, and law to psychology.
Variable is a simple something that can vary and can be measured: that is, it can take on many different values or categories.
Independent Variable are what we expect will influence dependent variables.
Dependent Variable is what happens as a result of the independent variable.
Control Variable is anything that is held constant or limited in a research study. It's a variable that is not of interest to the study's aims but is controlled because it could influence the outcomes.
Psychological variables encompass a large range of factors relating to an individual's psychological state and social environment and potentially have either positive and negative consequences for health and behavioral outcomes.
Descriptive statistics simply gives a general picture of the scores in a given group and they include the measures of central tendency and the measures of variability.
Central tendency involves different kinds of averages: the mean, median, and mode. Variability involves the standard deviation, which indicates how far scores in a group are likely to be from the average.
Inferential Statistics uses to help psychologists draw inferences, or conclusions, from the data obtained from their research.
Most common statistical test include the student's T-test and the analysis of variance (or F-test).
Population is the set of all individuals of interest in a particular study while sample is a set of individual selected from a population, usually intended to represent the population in a research study.
Parametric statistics refers to a branch of statistics that makes certain assumptions about the parameters of a population distribution from which the sample is drawn - powerful and provides presice estimates.
Normal Distribution is where the data is assumed to follow a probability distribution.
Homogeneity of Variance is the variance within each group being compared to be equal.
Independence is where the observations or samples are independent of each other.
Non-parametric statistics refers to a branch of statistics that does not make strong assumptions about the underlying population distribution.
Level of measurement is a kind of scale used to measure a response.
Nominal is the simplest level of measurement and involves categorizing data into distinct categories or groups without any specific order or hierarchy.
Ordinal is the value of the numerical scores tell us which is the smallest, the next smallest, and so forth up to largest - there is a rank or ordering of responses.
Interval measures magnitude or quantitative sie using measures with equal intervals between the values - it has no true zero point.
Ratio is the exactly the same as interval scale measure with one important priviso - absolute zero that is meaningful/has true zero.