How does inference different from prediction

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. The experiment will determine if the hypothesis is true. Example: Television results in violent behavior in children. This can or cannot be true, and will require an experiment. Making guesses based on information that is provided by the author and what is known of the character.

Making a guess about what is going to happen next in the book or towards the end, which can be proved at the end of the book. Science: Sherry's toddler is in bed upstairs. She hears a bang and crying. Sherry can infer that her toddler fell out of bed. Difference between Inference and Prediction. Key Difference: An inference in general can be defined as drawing conclusions based on observations using the five senses.

Inference can be understood as the process of working out from available information. On the other hand, Prediction is stating that an event will happen in the future.

This highlights that the key difference between inference and prediction stem from the fact that while prediction is mere foretelling, in inference, it is not so. Inferring denotes coming to conclusions with available evidence. Through this article let us examine the differences between the two words in depth.

In this case, the individual arrives at conclusions based on the information that he has. This highlights that the individual cannot draw conclusions without evidence or based on mere reason. In page 20 of the book, the authors provide a beautiful example which made me understand the difference. Here's the paragraph from the book : An Introduction to Statistical Learning. In this case one might be interested in how the individual input variables affect the prices—that is, how much extra will a house be worth if it has a view of the river?

This is a inference problem. Alternatively, one may simply be interested in predicting the value of a home given its characteristics: is this house under- or over-valued? This is a prediction problem. Generally when doing data analysis we imagine that there is some kind of "data generating process" which gives rise to the data, and inference refers to learning about the structure of this process while prediction means being able to actually forecast the data that come from it.

Oftentimes the two go together, but not always. But there are other types of models where one is able to make sensible predictions, but the model doesn't necessarily lead to meaningful insights about what is happening behind the scenes. Some examples of these kinds of models would be complicated ensemble methods which can lead to good predictions but are sometimes difficult or impossible to understand. Prediction uses estimated f to forecast into the future.

You want to make financial plans for your business, and need to forecast the revenue in next quarter. Now, if you get the data on income, say personal disposable income series from BEA, and construct the time of year variable, you may estimate the function f , then plug the latest values of the population income and the time of the year into this function.

This will yield the prediction for the next quarter of the revenue of the store. Inference uses estimated function f to study the impact of the factors on the outcome, and do other things of this nature.

In my earlier example you might be interested in how much the season of the year determines the revenue of the store. Prediction and inference may use the same estimation procedure to determine f , but they have different requirements to this procedure and incoming data.

A well-known case is so called collinearity , whereas your input variables are highly correlated with each other. For instance, you measure weight, height and belly circumference of obese people.

It is likely that these variables are strongly correlated, not necessarily linearly though. It happens so that collinearity can be a serious issue for inference , but merely an annoyance to prediction. For prediction this doesn't matter, all you care is the quality of the forecast. You are not alone here. 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.