Significance Level: The significance level, also known as alpha (ฮฑ), determines how much evidence we need against the null hypothesis before rejecting it. It's related but not equivalent to the desired confidence level.
Standard Error: The standard error measures how accurately our sample statistic estimates the population parameter. It takes into account both variability in data and sample size.
Type I Error: In hypothesis testing, a type I error occurs when we reject a null hypothesis that is actually true. It represents false positive results and corresponds with setting an incorrect significance level.