Experiments aim to discover universal laws of behavior that can be applied to large groups of people across different situations. Think of these laws as a guidebook that helps us predict human behavior!

Real-World Example: It's like how a cook follows a recipe to bake a cake. The recipe, in this case, can be considered a 'universal law' that when followed, regardless of where or by whom, should ideally produce a delicious cake!

A 'sample' in an experiment is a subset of people who participate, while the 'target population' refers to the broader group to which the findings are meant to be applied. The relationship is like a piece of puzzle (sample) fitting into the whole picture (target population).

Real-World Example: Imagine you want to know the favorite ice cream flavor of all high school students in your city (target population). You might poll students at your school (sample) to get an idea.

A representative sample reflects all the key characteristics of the target population, making the results applicable or "generalizable" to that larger group. It’s like picking a mini version of your target population for your experiment!

Real-World Example: If you want to know the most popular book amongst teenagers, your sample should include teenagers from various cultural backgrounds, socioeconomic statuses, and school types, just like the real-world variety!

The participant characteristics that are essential depend on the aim of the research. Factors like cultural background, socioeconomic status, and type of school can significantly influence the study.

Real-World Example: Studying the impact of video games on students' grades will require considering factors like the availability of game consoles (linked to socioeconomic status), cultural acceptance of gaming, etc.