Probability is the theoretical basis that underpins statistics.
Yes; the p value used in hypothesis testing is probability. See the related link.
Why are measures of variability essential to inferential statistics?
There are two types of statistics. One is called descriptive statistics and the other is inferential statistics. Descriptive statistics is when you use numbers. Inferential statistics is when you draw conclusions or make predictions.
Descriptive statistics are meant to describe the situation such as the average or the range. Inferential statistics is used to differentiate between a couple of groups.
Inferential statistics are used in situations where it can be assumed that random behaviour(s), subject to the mathematical laws of probability, must be taken into account.
Probability is the theoretical basis that underpins statistics.
Yes; the p value used in hypothesis testing is probability. See the related link.
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.
Why are measures of variability essential to inferential statistics?
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.
There are two types of statistics. One is called descriptive statistics and the other is inferential statistics. Descriptive statistics is when you use numbers. Inferential statistics is when you draw conclusions or make predictions.
Descriptive and Inferential Statistics
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.