Statistical Inference

Confidence Level : It gives the percentage (probability) of samples where the population mean would remain within the confidence interval around the sample mean. If is the significance level the confidence level is (1- ).
Contingency Table : A two-way table to present bi-variate data. It is called Statistical Inference contingency table because we try to find whether one variable is contingent upon the other variable.
Degrees of Freedom : It refers to the number of pieces of independent information that are required to compute some characteristic of a given set of observations.
Estimation : It is the method of prediction about parameter values on the basis of sample statistics.
Expected Frequency : It is the expected cell frequency under the assumption that both the variables are independent.
Nominal Variable : Such a variable takes qualitative values and do not have any ordering relationships among them. For example, gender is a nominal variable taking only the qualitative
values, male and female; there is no ordering in ‘male’ and ‘female’ status. A nominal variable is also called an attribute.
Parameter : It is a measure of some characteristic of the population.
Population : It is the entire collection of units of a specified type in a given place and at a particular point of time.
Random Sampling : It is a procedure where every member of the population has a definite chance or probability of being selected in the sample. It is also called probability sampling. Random sampling could be of many types: simple random sampling, systematic random sampling and stratified random
Sample : It is a sub-set of the population. It can be drawn from the population in a scientific manner by applying the rules of probability so that personal bias is eliminated. Many samples can be drawn from a population and there are many methods of drawing a sample.
Sampling Distribution : It is the relative frequency or probability distribution of the values of a statistic when the number of samples tends to infinity.
Sampling Error : In the sampling method, we try to approximate some feature of a given population from a sample drawn from it. Now, since in the sample all the members of the population are not included, howsoever close the approximation is, it is not identical to the required population feature and some error is committed. This error is called the sampling error.
Significance Level : There may be certain samples where population mean would not remain within the confidence interval around sample mean. The percentage (probability) of such cases is called significance level. It is usually denoted by When = 0.05 (that is, 5 percent) we can say that in
5 per cent cases we are likely to reach an incorrect decision or commit Type I error. Level of significance could be at any level but it is usually taken at 5 percent or 1 percent level.
Statistic : It is a function of the values of the units that are included in the sample. The basic purpose of a statistic is to estimate some population parameter.

Source:IGNOU Study Material

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