Therefore, generalizability is neverstatistically appropriate.Non Probability Sampling 44. These occur when the analytical test is not carried out correctly: the wrong chemical reagent or equipment might have been used; some of the sample may have been spilt; a volume Sample location. a tanker of well stirred liquid oil), whereas a heterogeneous population is one in which the properties of the individual samples vary with location (e.g.

When rounding numbers: always round any number with a final digit less than 5 downwards, and 5 or more upwards, e.g. 23.453 becomes 23.45; 23.455 becomes 23.46; 23.458 becomes 23.46. How well the straight-line fits the experimental data is expressed by the correlation coefficient r2, which has a value between 0 and 1. Exact numbers have an infinite number of significant digits. A compartmentalized population is one that is split into a number of separate sub-units, e.g., boxes of potato chips in a truck, or bottles of tomato ketchup moving along a conveyor

Why not share! Many times you will find results quoted with two errors. Then, imagine increasing the sample size to 100, the tendency of their scores is to cluster, thus a low standard deviation. . P: The true population proportion.

For example, 12.312 (5 significant figures) x 31.1 (3 significant figures) = 383 (3 significant figures). SAMPLE•It is a Unit that selected from population•Representers of the population•Purpose to draw the inference 4. Very difficult to study each and every unit of thepopulation when population unit are heterogeneousWHY The closer the value is to 1 the better the fit between the straight line and the experimental values: r2 = 1 is a perfect fit. The most frequent cause of the said error is a biased sampling procedure.

However, if Z = AB then, , so , (15) Thus , (16) or the fractional error in Z is the square root of the sum of the squares of the This solution is to eliminate the concept of sample, and to test the entire population.In most cases this is not possible; consequently, what a researcher must to do is to minimize Types of Non probability Sampling Purposive Sampling Quota sampling (larger populations)Snowball samplingSelf-selection samplingConvenience sampling 46. Key Points on ErrorsNon-sampling errors are inevitable in production ofnational statistics.

If the variables are independent then sometimes the error in one variable will happen to cancel out some of the error in the other and so, on the average, the error Non-sampling error[edit] Sampling error can be contrasted with non-sampling error. These individual errors accumulate to determine the overall error in the final result. Stratified Random Sampling 29.

Or, 1310 (last significant figure in the "10" decimal column) + 12.1 (last significant figure in the "0.1" decimal column) = 1320 (last significant figure in the "10" decimal column). Simple random sampling 21. If the observations are collected from a random sample, statistical theory provides probabilistic estimates of the likely size of the sampling error for a particular statistic or estimator. And then, we compute the standard error (SE).

For example, 400. And in order to draw valid conclusions the error must be indicated and dealt with properly. So one would expect the value of to be 10. Louis, MO: Saunders Elsevier.

A continuous population is one in which there is no physical separation between the different parts of the sample, e.g., liquid milk or oil stored in a tanker. View Mobile Version ERROR The requested URL could not be retrieved The following error was encountered while trying to retrieve the URL: http://0.0.0.8/ Connection to 0.0.0.8 failed. Therefore, if the sample has high standard deviation, it follows that sample also has high sampling process error.It will be easier to understand this if you will relate standard deviation with It does not necessarily have to correspond to the true value of the parameter one is trying to measure.

But in the end, the answer must be expressed with only the proper number of significant figures. Assume a 95% confidence level. The critical value is a factor used to compute the margin of error. The meaning of this is that if the N measurements of x were repeated there would be a 68% probability the new mean value of would lie within (that is between

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