There is generally some difference between a statistic i.e. the value obtained from a sample and its corresponding parameter of the population. In above example, discussed the mean of the weights of 200 students taken (first sample) is slightly different from the mean of the weights of 4000 students i.e. the population. This difference is called sampling error (whose types and situations are discussed). This error can be minimized by increasing the size of the sample and by taking sample of extremely random in nature. If the size of sample is large the sampling error is small. The accuracy of the sample increases as the square root of the size 'n' of the sample i.e. according to Ön. Hence we take samples of sufficiently large in size.

Index

8.1 Population
8.2 Sample
8.3 Parameters and Statistic
8.4 Sampling Distribution
8.5 Sampling Error
8.6 Central Limit Theorem
8.7 Critical Region
8.8 Testing of Hypothesis
8.9 Errors in Tesitng of Hypothesis
8.10 Power o a Hypothesis Test
8.11 Sampling of Variables
8.12 Sampling of Attributes
8.13 Estimation
8.14 Testing the Difference Between Means
8.15 Test for Difference Between Proportions
8.16 Two Tailed and one Tailed Tests
8.17 Test of Significance for Small Samples
8.18 Students t-distribution
8.19 Distribution of 't' for Comparison of Two Samples Means Independent Samples
8.20 Testing Difference Between Mens of Two Samples Dependent Samples or Matched Paired Observations
8.21 Chi-Square
8.22 Sampling Theory of Correlation
8.23 Sampling Theory of Regression

Chapter 1