Everyone conducting research is looking for associations between predictor, confounding, and outcome variables. People will often talk about wanting to know the correlation, relationship, or effects between variables.  These types of research questions are answered using observational research designs.

However, when researchers are attempting to explain causal effects, there are certain criteria that have to be met.  

1. An experimental design is the only method by which a causal effect can be inferred. This means that study participants are selected at random and randomly assigned to treatment groups. Any differences at baseline are thought to occur by chance with random selection and random assignment (whereas with observational designs they are due to selection and observation biases).  

2. Analyses have to be conducted in an "intention-to-treat" fashion. This means that all study participants are analyzed in the original groups that they were randomly assigned to at the beginning of the study.

3. Blinding can bolster the validity of causal effects, but is not necessary for establishing causal effects. Blinding can reduce observational biases that can occur in experiments where human beings are studied.    

4. As much as possible, all pertinent demographic, clinical, and confounding variables related to the association between the primary predictor and primary outcome variable should be accounted for in experiments. True causal effects (in reality and practice) are always multivariate in nature. Clinicians do not make treatment decisions based on one form of evidence, they want as many forms of evidence as possible. The statistics that establish the efficacy of treatments should reflect the multivariate nature of treatment decisions.

5. There has to be a sufficient amount of time or follow-up for an outcome. True causal effects are found when the temporal aspects (etiological, prognostic, time) of developing an outcome are accounted for methodologically and statistically.

6. One clinical trial that shows valid evidence of a causal effect and meets all of the aforementioned criteria is STILL NOT ENOUGH! The results of the most methodologically sound randomized controlled trials or true experiments need to be aggregated in systematic reviews, syntheses, and synopses of syntheses. Pooled effects account for more variance in the general population and strengthen the "causal" understanding of causal effects.  

Experimental research designs, like randomized controlled trials, can yield evidence of causal effects while observational designs like case series, case-controls, and cohorts cannot determine any cause and effect relationships. The reason is because random selection and random assignment of participants allows for any differences at baseline to occur purely by chance AND also for these differences to be adjusted for in subsequent statistical analyses.

From a conceptual standpoint, a sample assembled in a completely random fashion will be more REPRESENTATIVE of the actual population. Always remember that inferential statistics are conducted on samples to make INFERENCES BACK TO THE POPULATION. With a randomized sample, all of the biodiversity that exists in the real world has a better chance of being accounted for in the statistical analyses.  

Random selection (every member of a given population has an equal chance of being selected for the study) and random assignment (selected participants are randomly allocated to either the treatment or control group) are the primary components of probability sampling.

There are three types of probability sampling:

1. Simple random sampling - Every member of a population has an equal chance of being selected for participation in the study.  

2. Stratified random sampling - Independent strata within a given population are randomly sampled.  Each stratum must be overtly defined and homogeneous in some relative way.  Simple random sampling is then conducted on the stratum (singular) or strata (plural) of interest. 

3. Clustered random sampling - Naturally occurring or defined subgroups of a given population are randomly sampled. The subgroups need to be defined and are often grouped according to socioeconomic, demographic, clinical, or theoretical characteristics.

Non-probability sampling is used in observational research designs. The lack of randomization in these designs introduces selection and observation biases that can greatly skew the inferences yielded from statistics.

There are two types of non-probability sampling techniques:

1. Convenience sampling is the most prevalent form of non-probability sampling. Researchers just access retrospective data available to them in their empirical or clinical environment, or via existing databases, and conduct statistical analyses.

2. Purposive sampling is a more focused approach to sampling where specific groups of individuals are targeted for participation in the study.