Simple Random Sample (SRS): A sampling method where every member of the population has an equal chance of being selected. Think of it like drawing names out of a hat where each name is equally likely to be picked.

Convenience Sample: A sample taken from a group that is easy to reach or contact. It’s not random and might not represent the entire population well. For example, asking only your friends about their favorite snacks.

Voluntary Response Sample: A sample that relies on individuals volunteering to participate, usually through surveys or polls. People with strong opinions are more likely to respond, which can lead to bias.

Stratified Random Sample: A sampling method where the population is divided into subgroups (strata) that share similar characteristics. Then, a simple random sample is taken from each subgroup to ensure all groups are represented.

Cluster Sample: The population is divided into clusters, or groups, and then entire clusters are randomly selected. All members within the chosen clusters are included in the sample.

Systematic Random Sample: A sample where you select every "kth" individual from a list of the population. For instance, if you choose every 5th person on a list, you’re using a systematic random sample.

Undercoverage: A sampling error where some groups in the population are left out or underrepresented in the sample. This can lead to biased results if the missing groups differ significantly from the sample.

Non-Response: Occurs when individuals chosen for the sample do not respond or cannot be contacted. This can lead to bias if the non-respondents differ from the respondents in important ways.

Response Bias: When the way a question is phrased or the environment in which it is asked influences responses. This can result in inaccurate or skewed data.

Confounding Variables: Extra variables that can affect the relationship between the independent and dependent variables. In experiments, they can make it hard to determine what truly caused the outcome.

Observational Study: Observes and measures variables without influencing them. It can reveal associations but cannot prove causation.

Experiment: Actively applies treatments to experimental units to measure effects. Experiments can help establish causation because they involve controlled conditions and random assignment.

Experimental Units: The individuals or objects that receive the treatment in an experiment. If they are humans, they’re often called "participants" or "subjects."

Treatments: The different conditions applied to the experimental units. A treatment might involve giving one group a new medication while another receives a placebo.

Components of an Experiment: Key components include experimental units, treatments, control groups, random assignment, and measurements of the response variable.

Random Assignment: Assigning experimental units to treatments randomly. This reduces bias and ensures each group is similar before treatments are applied, helping to isolate treatment effects.

Placebo Effect: A psychological effect where participants experience improvements simply because they believe they are receiving treatment, even if they aren’t (like a sugar pill instead of real medicine).

Randomized Block Design: Experimental design where subjects are first divided into blocks based on similar characteristics, then randomly assigned to treatments within each block. This helps control for variables that could affect results.

Matched Pairs Design: Experimental design where subjects are paired based on similar characteristics, and one member of each pair is assigned each treatment. This controls for differences between subjects and allows for more precise comparisons.

Simulation: A method that models a real-life situation to study how different outcomes could occur. It’s often done using random numbers to mimic real-world randomness.

Statistical Significance: When an observed effect is so large or consistent that it’s unlikely to have happened by chance. It suggests that the result is meaningful.

Purpose of Using a Random Sample: To ensure that every individual in the population has an equal chance of being chosen, which makes the sample more representative and reduces bias.

Purpose of Using Random Assignment: To ensure that differences in treatment outcomes can be attributed to the treatments themselves rather than to other variables. Random assignment helps establish causation in experiments.