A cause-effect relationship involves a particular independent variable affecting the dependent variable of interest (the effect) or an event (the cause) making another event happen (the effect). One cause can have several effects. In an experimental approach, the researcher carefully controls the treatment/cause/the independent variable and then measures the outcome of interest/effect/the dependent variable. So, there is a direct and strong cause and effect relation. In a correlational approach, positive (directly proportional) or negative (inversely proportional) relation is studied in the two variables. Descriptive statistics seeks to describe the data collected from the sample to the whole population unlike inferential statistics as opposed to inferential statistics that attempts to make inferences. Non-experimental research, unlike experimental research generally cannot provide strong evidence that changes in an independent variable can cause differences(effects) in a dependent variable.