Example Of Descriptive Statistics
Averages are used everywhere, every day in life, from sports to the classroom. Descriptive statistics, however, can only be used for a particular set of data. The numbers I got from question one above cannot be used for any other classroom, even if it is right next door and a few degrees below. Group A’s set of data is group a’s set of data. Descriptive statistics are the easiest statistics to understand a data set but just remember they can only be used for that particular set of data.
3. Statistics that can used be to generalize a population or multiple sets of data is called inferential statistics. This is other type of statistic not dealing with mean, median, and mode. These types of statistics can be looked upon as much more difficult. But the statistics hold a much stronger baring that allow a researcher to infer trends about a larger …show more content…
When it comes down to errors in stats, alpha and beta help control the errors. From our notes, alpha means the mistake of accepting a hypothesis when the null should have been controlled statistically, typically at the .05 level. Beta means the mistake of choosing the null when your hypothesis is at work (is remaining %). Alpha and beta are easier to spot in data and formula as they are in Greek lettering. Alpha is used for a Type 1 error while Beta is a Type 2 error. Alpha and beta can range from 0 to 1 where 0 means there is no chance of making a Type 1 or Type 2 error and 1 means it is unavoidable. Per more research on beta, the population regression coefficients in problems and research are denoted by beta. Alpha is not calculated but decided upon. Researchers can use either alpha or beta but throughout research history, alpha has been the favorite. Another definition for alpha is “Acceptable probability for rejecting the null hypothesis while it is true.” It’s a complicated process but alpha and beta serve as the backbone for error in the hypothesis. In the simplest terms, Type 1 error, alpha, is comparable to false positives. This is thinking you have it right when in reality you don’t. Type 2 in simplest terms, beta, is the same as false negatives. Meaning you may think that your experiment had no effect on the variable but in reality it did. Alpha is considered a more desirable error than beta because at least with alpha, the attempt will be made. Sometimes in