What are the causes of arising statistical errors while observing a statistical work

Publish On: 2019-01-19

Total Post: 752

Gaurabh Raghav

Total Post: 33

ANS: What are the causes of arising statistical errors while observing a statistical work

Many types of technical errors are possible in statistical works which would have the effect to arriving at wring conclusions from the data. For example, errors may be committed in the choice of a suitable formula, such as arithmetic mean may be used in a situation where harmonic mean is more appropriate. Similarly arithmetical errors may also be committed while classifying the data or analyzing the data. Errors in units of measurement are also common. A frequent error of this kind is to confuse two kinds of logarithms, ‘’natural’’ and common’’, the firmer being 2-3 tines the Katter. Another type of error that generally prevails in statistical work is in the use of ration a percentages. While using theses either a wring base is used or 100 is, misunderstood. A comparison of percentages without knowledge of the vase to which they refer would lead to error and confusion. For example, let us take a case in which there are three industries dominating the economic life of a local community. One of them expects in 2010 employment 10 per cent below normal, another 15 per cent below normal, butyl the third expects employment 25 per cent above normal. Should it be concluded from this industry would be in a position to absorb those displaced by the other two? This type of comparison without knowledge of the base is misleading. We must know the number of workers knowledge of the base is misleading, we must know the number of workers employed in the two industries is 10,000 and 5,000 the number of workers unemployed would be 1,750. Now suppose the third industry normally employs 6,000 persons. An increase of 25 per cent would mean 1,500 more jobs. This means that still 250 people would be unemployed. Hence knowledge of base is absolutely essential while comparing percentages.

Quite often 100 is not subtracted in figuring increases and this leads to wrong conclusion. For example, the price of a commodity has increased from \$ 50 in 1989 to \$ 150 in 2009 and hence one may say that there is 300 per cent increase in price. However, a little thinking would reveal that the actual increase is only 200 per cent.