What is the difference between predicting and inferring

Sc3 2 life cycles of plants. Plants powerpoint. Grade 5 module 5. Related Books Free with a 30 day trial from Scribd. Related Audiobooks Free with a 30 day trial from Scribd. Melodie Villanueva. Lara Jordan. Ameer Hamza. Mary Hudson , teacher at Troup County Schools.

Claudia Pina. Show More. Views Total views. Actions Shares. No notes for slide. Prediction And Inference 1. Cowan 2. What is prediction? What is inference? What is the difference between prediction and inference?

When inferring, you may or may not know the answer to your question by the end of the story. When inferring, you are making a guess about what a character will do, how a character feels, and other judgments. When do you make predictions?

When do you make inferences? These inferences may not be about what will happen next. How do you make predictions? After reading answers, I am not confused anymore - not because I understand the difference, but because I understand it is in the eye of the beholder and verbally induced. I am sure now those two terms are political definitions rather than scientific ones.

Take for example the explanation from the book, the one that colleges tried to use as a good one: "how much extra will a house be worth if it has a view of the river? You are civil construction company owner, and you want to choose the best ground for building next set of houses. You have to choose between two location in the same town, one near the river, the next near the train station. You want to predict the prices for both locations. Or you want to infer. You are going to apply the exact methods of statistics, but you name the process.

Imagine, you are a medical doctor on an intensive care unit. You have a patient with a strong fever and a given number of blood cells and a given body weight and a hundred different data and you want to predict, if he or she is going to survive. If yes, he is going to conceal that story about his other kid to his wife, if not, it is important for him do reveal it, while he can. The doctor can do this prediction based on the data of former patients he had at his unit.

Based on his software knowledge, he can predict using either a generalized linear regression glm or via a neural net nn. There are far to many correlated parameters for the glm so to get to a result, the doctor will have to make assumptions linearity etc.

The glm will reward him with a t-test of significance for each of his parameters so he might gather strong evidence, that gender and fever have a significant influence, body weight not necessarily so. The neural net will swallow and digest all information that there is in the sample of former patients.

It will not care, whether predictors are correlated and it will not reveal that much information, on whether the influence of body weight seems to be important only in the sample at hand or in general at least not at the level of expertise that the doctor has to offer.

It will just compute a result. What method to choose depends on the angle from which you look on the problem: As a patient, I would prefer the neural net which uses all available data for a best guess on what will happen to me without strong and obviously wrong assumptions like linearity.

As the doctor, who wants to present some data in a journal, he needs p-values. Medicine is very conservative: they are going to ask for p-values. So the doctor wants to report, that in such a situation, gender has a significant influence. For the patient, that does not matter, just use whatever influence the sample suggests to be most likely. In this example, the patient wants prediction, the scientist-side of the doctor wants inference.

Mostly, when you want to understand a system, then inference is good. If you need to make a decision where you cannot understand the system, prediction will have to suffice.

This question does not ask anything about the parameters in the true model. I know many answers have been posted already, but for those of you who don't read the book Introduction to Statistical Learning , here's three exercises found in the second chapter. See if you can solve them, they helped me quite a bit to understand the difference between inference and prediction.

Explain whether each scenario is a classification or regression problem, and indicate whether we are most interested in inference or prediction. We collect a set of data on the top firms in the US. For each firm we record profit, number of employees, industry and the CEO salary. We are interested in understanding which factors affect CEO salary.

We are considering launching a new product and wish to know whether it will be a success or a failure. We collect data on 20 similar products that were previously launched.

For each product we have recorded whether it was a success or failure , price charged for the product, marketing budget, competition price, and ten other variables. Hence we collect weekly data for all of If you want the answers, they can be found here. Note that the exercise above is number 2.

There's good research showing that a strong predictor of whether borrowers will repay their loans is whether they use felt to protect their floors from being scratched by furniture legs.

This "felt" variable will be a distinct aid to a predictive model where the outcome is repay vs. However, if lenders want to gain greater leverage over this outcome, they will be remiss in thinking they can do so by distributing felt as widely as they can. Prediction example : suppose y represent the salary of a person then if we provide input such as years of experience, degree as input variables then our function predicts the salary of the employee.

Sign up to join this community. The best answers are voted up and rise to the top. Stack Overflow for Teams — Collaborate and share knowledge with a private group. Prediction is a forecast about a future event or a happening. This is similar to foretelling.

A prediction is not generally based on evidence or clues. This can be based on past experience or reasoning. This technique is also widely used in reading comprehension passages. Here, the students are making predictions without proper information. Inference : Inference is forecasting about a future event with the help of available evidence.

Prediction : Prediction is a forecast about future. Inference : A future event is inferred from looking at the evidence i.