CÓMO OBTENER UNA MUESTRA CON POBLACIÓN FINITA E INFINITA

Summary

In this video, the speaker explains how to calculate sample sizes for qualitative variables in social sciences research. The focus is on the distinction between infinite and finite populations, with formulas provided for each case. The process includes determining the level of confidence (typically 95%) and the margin of error. The speaker walks through an example of surveying used clothing sellers, calculating the required sample size for an unknown population. Additionally, the video covers variations in confidence levels and margins of error, offering a step-by-step guide for applying these formulas to real-world research scenarios.

Takeaways

• 😀 **Understanding Probabilistic Sampling**: The script explains probabilistic sampling, which uses statistical formulas to select a representative sample from a population, divided into quantitative and qualitative data types.
• 😀 **Focus on Qualitative Variables**: The video emphasizes the importance of working with qualitative variables, especially in social sciences research, to gain insights into population characteristics.
• 😀 **Infinite vs Finite Populations**: The difference between infinite populations (where the total number is unknown) and finite populations (where the total number is known) is highlighted for proper sample calculation.
• 😀 **Formula for Infinite Populations**: For infinite populations, the sample size can be calculated using a formula involving the confidence level, probability of positive and negative occurrences, and the margin of error.
• 😀 **Formula for Finite Populations**: When dealing with finite populations, the formula includes the same elements as the infinite population formula, but adjusted for population size and margin of error.
• 😀 **Confidence Level of 95%**: A 95% confidence level is commonly used in social science research. This corresponds to a Z-value of 1.96 in statistical calculations.
• 😀 **Error Margin of 5%**: A 5% margin of error is often chosen in studies, meaning the sample's estimates will be within 5% of the true population values.
• 😀 **Practical Example with Infinite Population**: An example is given where the total number of second-hand clothing vendors in a city is unknown. The sample size for such a scenario is calculated to be 384 based on a 95% confidence level and 5% margin of error.
• 😀 **Adjustments Based on Prior Research**: If previous studies are available, the confidence level, margin of error, and occurrence probabilities can be adjusted based on the new context or population being studied.
• 😀 **Applying Confidence Levels and Error Margins**: The script walks through a practical example of a study in sociology, using a 97% confidence level and a 3% margin of error to calculate the sample size required for a population of 700 students, which comes out to 456 surveys.
• 😀 **Importance of Accurate Calculation**: Correct calculation of sample size is crucial to obtaining reliable data, especially in quantitative research, as it ensures the results reflect the true characteristics of the population within the defined confidence and error margins.

Q & A

What is the main focus of the video script?

The video script focuses on explaining how to calculate sample sizes for surveys, particularly when working with qualitative variables in social science research. It outlines the formulas for both infinite and finite populations.

What are the two types of variables discussed in the video?

The two types of variables discussed are quantitative variables, which are numerical, and qualitative variables, which are categorical. The video primarily focuses on qualitative variables.

How is the population classified in the context of this video?

The population is classified into two types: infinite populations, where the exact number of people is unknown (e.g., people who consume alcohol), and finite populations, where the exact number of individuals is known (e.g., a specific class of students).

What is the formula used for calculating sample size in infinite populations?

The formula for calculating sample size in infinite populations is: n = (Z^2 * p * (1 - p)) / E^2 where Z is the Z-score, p is the proportion, and E is the margin of error.

What formula is used for finite populations, and how does it differ from the infinite population formula?

The formula for finite populations is: n = (Z^2 * p * (1 - p) * N) / ((N - 1) * E^2 + Z^2 * p * (1 - p)) This formula includes the total population size (N) and adjusts for the finite nature of the population.

Why is 0.50 typically used as the proportion (p) in the sample size formula?

The value of 0.50 is used as the proportion because it represents the most conservative estimate. When the true proportion is unknown, using 0.50 maximizes the required sample size, ensuring that the sample size is large enough to be reliable.

What confidence level and margin of error are used in the example of infinite population?

In the example with an infinite population, a 95% confidence level is used, which corresponds to a Z-score of 1.96. The margin of error is 5% (0.05).

What result is obtained when calculating the sample size for the infinite population example?

For the infinite population example, the sample size calculated is 384. This means 384 surveys are needed to achieve a 95% confidence level with a 5% margin of error.

How is the sample size calculated for the finite population of 700 students?

For a finite population of 700 students, with a 97% confidence level (Z = 2.17) and a 3% margin of error (E = 0.03), the calculated sample size is 456.

What is the importance of adjusting the sample size formula based on the confidence level and margin of error?

Adjusting the sample size formula based on the confidence level and margin of error is crucial because these factors directly affect the reliability and precision of the survey results. Higher confidence levels and smaller margins of error require larger sample sizes to maintain accuracy.