The question is asking about the type I error when the sample size is n = 100. Unfortunately, the search results do not provide a clear context for this question, so it is difficult to give a definitive answer. However, we can provide some general information about type I error and sampling.

Type I error is a statistical term that refers to the rejection of a true null hypothesis. In other words, it occurs when we conclude that there is a significant difference between two groups when there is actually no difference.

The probability of making a type I error is denoted by alpha (α). It is typically set at 0.05 or 0.01, which means that there is a 5% or 1% chance of making a type I error, respectively. The smaller the alpha level, the less likely we are to make a type I error.

Sampling is the process of selecting a subset of individuals from a larger population to be included in a study. The sample size is the number of individuals in the sample. The larger the sample size, the more representative it is likely to be of the population.

There are different sampling techniques that can be used depending on the research question and the characteristics of the population. Some common sampling techniques include:

- Simple random sampling: each individual in the population has an equal chance of being selected for the sample.

- Stratified random sampling: the population is divided into strata based on certain characteristics (e.g., age, gender) and individuals are randomly selected from each stratum.

- Cluster sampling: the population is divided into clusters (e.g., schools, neighborhoods) and individuals are randomly selected from each cluster.

In conclusion, to answer the question about type I error when the sample size is n = 100, we would need more information about the study design and research question. However, we can provide some general information about type I error and sampling, which may be useful for understanding the concepts more broadly.