A real estate company has developed a computer algorithm, that predicts returns on land to build apartment buildings on. If the algorithm predicts high profitability, the company buys the land and starts building. If, instead the algorithm predicts low or mediocre return, the algorithm will not buy the land. The decision making procedure can be described as a statistical test by estimating the location in the null hypothesis unprofitable and, in the alternative hypothesis, profitable again.

1. Explain the practical risks involved in making a Type I error in this situation (So - called-risk.)
2. Explain the practical risks involved in making a Type II error in this situation (So - called-risk.)
3. How does the changing the rejection criteria affect Type I and Type II errors probabilities?
4. How the concepts of risk level, significance level and confidence level are related to each other?