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.

If the sample isn't representative, we can either keep adding to it until it becomes representative, or we narrow down the target population, making the findings less generalizable but more accurate.

Real-World Example: If studying ice cream preferences at a school doesn't reflect city-wide tastes, you could either survey students at other schools or limit your study's focus to your own school.

There's no definitive quantitative way to establish representativeness. A researcher decides whether a characteristic is essential based on theories and prior research. It's a bit like using a compass to guide your way in a jungle!

Different methods can be used to select participants:

• Random sampling: Here, everyone in the target population has an equal chance of being selected. It's like picking names out of a hat!
  • Real-World Example: Imagine selecting phone numbers randomly for a pre-election survey.
• Stratified sampling: This method involves identifying essential characteristics, studying their distribution in the target population, and maintaining these proportions in the sample.
  • Real-World Example: If studying smoking habits across different ages, your sample should have a proportionate number of people from each age group.
• Convenience sampling: This involves choosing participants who are most easily available, often leading to a lack of representativeness.
  • Real-World Example: Psychology research often involves university students since they're readily available to the researchers (typically professors).
• Self-selected sampling: In this method, volunteers are recruited, often through advertisements.
  • Real-World Example: Research involving online surveys often falls into this category as people voluntarily respond to the survey.

With a better understanding of these techniques, you can thoughtfully approach your study about the influence of praise on school performance amongst teenagers!