Statistical inference is about testing hypotheses. In order to test a hypothesis, you make a prediction about the observations, contrasting the prediction with what might happen if the hypothesis were not true. The prediction is tested against the observations by calculating a test statistic or inferential statistic. This is a value which is based purely on the observations. If the test statistic is too far from the predicted value then the hypothesis should be rejected in favour of the alternative hypothesis.
What constitutes "too far" depends on the presumed distribution of the variable being tested, as well as the degree of certainty required from the test - the power of the test. The latter is a balance between probability of rejecting the hypothesis when it is true and that of not rejecting it when it is false. These outcomes may be weighted according to the risk or costs that a false decision carries.
Chat with our AI personalities
There is no inferential data. There is inferential statistics which from samples, you infer or draw a conclusion about the population. Hypothesis testing is an example of inferential statistics.
Inferential statistics uses data from a small group to make generalizations or inferences about a larger group of people. Inferential statistics should be used with "inferences".
Descriptive statistics is a summary of data. Inferential statistics try to reach conclusion that extend beyond the immediate data alone.
yes
One advantage of inferential statistics is that large predictions can be made from small data sets. However, if the sample is not representative of the population then the predictions will be incorrect.