When someone/something becomes random. For instance, I would become random when I wasn't neccesarily previously random now by doing this:
gfhjasbgfhbasfbh./alkjsfkjhfjkrelahlwrekguhgaerilufraheufaherluarluehalue
dfjvhj,sdfbvjsbbhbs\dhjbhsgbfdjsdvbkfgvfdbbhfbg,vszfbvsdhjz,bjdhzz
sfgsdfgbajsfdavjfvr4barxvcevserarbfgbvfbdgwqertyuiopgfdsfghjygtfrdtyuiktgfr
YES
True
true
Randomization in selecting a sample helps ensure that every individual in the population has an equal chance of being chosen, which minimizes bias and enhances the representativeness of the sample. This process increases the validity of the study's results by allowing researchers to make more accurate generalizations about the population. Additionally, randomization helps control for confounding variables, making it easier to identify causal relationships. Overall, it strengthens the reliability of the findings.
Using more control variables instead of relying solely on randomization can lead to overfitting, where the model becomes too tailored to the specific dataset and loses its generalizability. Additionally, controlling for numerous variables can complicate analyses and introduce multicollinearity, making it difficult to ascertain the true effects of the independent variable. Randomization, on the other hand, helps ensure that extraneous variables are evenly distributed across treatment groups, allowing for a clearer causal inference. Ultimately, a balanced approach that combines both strategies may be most effective.
The common types of randomization include simple randomization, block randomization, and stratified randomization. Simple randomization involves assigning participants randomly to treatment groups with each having an equal chance of being selected. Block randomization involves grouping participants into blocks and then randomly assigning them to treatment groups within each block. Stratified randomization involves dividing participants into distinct subgroups based on specific criteria and then randomizing within each subgroup.
Yes.
YES
Assignment of persons by a method based on chance
True
The lack of randomization in a cohort study can lead to selection bias, where certain characteristics of participants are not evenly distributed between comparison groups. This can affect the internal validity of the study results, making it difficult to attribute observed differences to the exposure being studied rather than other factors. Randomization helps to control for potential confounding variables and ensures that differences in outcomes can be more confidently attributed to the intervention or exposure being investigated.
You don't waste time computing a pivot.
true
ensure taht the sample for the study is representative of the target population
Randomization in selecting a sample helps ensure that every individual in the population has an equal chance of being chosen, which minimizes bias and enhances the representativeness of the sample. This process increases the validity of the study's results by allowing researchers to make more accurate generalizations about the population. Additionally, randomization helps control for confounding variables, making it easier to identify causal relationships. Overall, it strengthens the reliability of the findings.
The primary purpose of correlational research is to explore relationships among variables to understand how they are related. It does not determine causation, make predictions, involve randomization, or have control groups.
False