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Understanding populations and samples is crucial in psychological studies for valid results. A population encompasses all individuals of interest, while a sample is a subset used for the study. Sampling techniques, like random sampling, aim to ensure representativeness and minimize bias, allowing researchers to generalize findings to the whole population. Challenges in data collection from large populations necessitate strategic sampling for meaningful insights.

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## Definition of Population and Sample

### Population

A population is the entire set of individuals or observations that are of interest to the researcher's question

### Sample

A sample is a smaller group drawn from the population that is representative and allows for the extrapolation of study findings

### Sampling

Sampling is the process of selecting a sample from a population, which can significantly impact the outcomes of a study

## Types of Sampling Techniques

### Probability Sampling

Probability sampling, such as random sampling, is the gold standard for selecting a representative sample from a population

### Non-probability Sampling

Non-probability sampling methods, such as convenience sampling or quota sampling, may introduce bias and limit the generalizability of results

## Challenges in Collecting Data from Large Populations

### Logistical Challenges

Collecting data from an entire population can be difficult due to logistical challenges, particularly with large populations

### Underrepresented Groups

Accurately counting underrepresented groups, such as low-income or minority populations, can be a challenge in data collection from large populations

## Importance of Samples in Psychological Research

### Generalizability

Samples allow researchers to make informed and statistically significant inferences about the larger population they are studying

### Representative Samples

Representative samples are crucial for generalizing research findings to the entire population

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