8.5 Sampling Error
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 tdistribution
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 ChiSquare
8.22 Sampling Theory of Correlation
8.23 Sampling Theory of Regression
Chapter 1
