Answer :
To learn more about the heights of buildings in New York City, choosing a suitable sample is crucial. There are several methods to ensure the sample is representative of the whole city.
### Sampling Methods:
1. Simple Random Sampling:
- In this method, you randomly select buildings from all across New York City. The randomness ensures that every building has an equal chance of being chosen, which helps in getting an unbiased representation of the entire city's building heights.
2. Stratified Sampling:
- In stratified sampling, you divide the city into different strata or groups based on certain characteristics, such as neighborhoods or building types. From each stratum, a random sample is selected. This method guarantees that each subgroup is represented in the overall sample.
### Representativeness of the Sample:
1. Simple Random Sampling:
- This method ensures that the sample is more likely to be representative of the entire city. Since every building has an equal chance of selection, the resulting sample can provide an unbiased estimate of the population parameters, such as mean height, height distribution, etc.
2. Stratified Sampling:
- Stratified sampling helps to ensure that key subgroups are adequately represented in the sample. For instance, if certain neighborhoods have vastly different building heights, this method ensures these variations are captured in the sample. This can lead to more precise and reliable estimates of the overall building heights.
### Potential Bias and Non-representative Samples:
- Non-representative Samples:
- If the sample is biased, it may not accurately reflect the characteristics of the city’s buildings. An example of a biased sample would be selecting buildings from only one neighborhood, which would not account for the diversity in building heights throughout New York City.
In conclusion, using methods such as simple random sampling or stratified sampling ensures that the sample of building heights is representative of New York City. This eliminates bias and helps achieve accurate insights into the height distribution of buildings across the city.
### Sampling Methods:
1. Simple Random Sampling:
- In this method, you randomly select buildings from all across New York City. The randomness ensures that every building has an equal chance of being chosen, which helps in getting an unbiased representation of the entire city's building heights.
2. Stratified Sampling:
- In stratified sampling, you divide the city into different strata or groups based on certain characteristics, such as neighborhoods or building types. From each stratum, a random sample is selected. This method guarantees that each subgroup is represented in the overall sample.
### Representativeness of the Sample:
1. Simple Random Sampling:
- This method ensures that the sample is more likely to be representative of the entire city. Since every building has an equal chance of selection, the resulting sample can provide an unbiased estimate of the population parameters, such as mean height, height distribution, etc.
2. Stratified Sampling:
- Stratified sampling helps to ensure that key subgroups are adequately represented in the sample. For instance, if certain neighborhoods have vastly different building heights, this method ensures these variations are captured in the sample. This can lead to more precise and reliable estimates of the overall building heights.
### Potential Bias and Non-representative Samples:
- Non-representative Samples:
- If the sample is biased, it may not accurately reflect the characteristics of the city’s buildings. An example of a biased sample would be selecting buildings from only one neighborhood, which would not account for the diversity in building heights throughout New York City.
In conclusion, using methods such as simple random sampling or stratified sampling ensures that the sample of building heights is representative of New York City. This eliminates bias and helps achieve accurate insights into the height distribution of buildings across the city.