Feedback if you agree or no to this statement? All of our decisions have an amou

Question

Feedback if you agree or no to this statement? All of our decisions have an amount of bias in them. Whether that bias is based on our own religious/ lack thereof of beliefs or political theories, it is always prevalent. The real question is how much of an impact that bias makes. The example in the video of searching for terrorists is a prime example of this. If you study history about radical Islam from the Barbary pirates to Hamas and Isis it will have an influence on how perceive a situation. Someone without that knowledge will not see things the same way. In reality, bias isn't always bad when it is routed in knowledge. You can never have too much data, because data provides insight and knowledge. Data mining can help in making the right choice by both challenging are biases or confirming them. Data doesn't really have bias, but it can show patterns and trends.

Explanation / Answer

I agree with the paragraph mentioned above.

The paragraph essentially touches upon two important points.

It is rightly said that data collected can only show a particular trend in a given condition/environment.That is why it is equaly important to mention the conditions in which the data was captured. Ideally, the limit of sample size is not as critical as the condition of data collection.We can always simulate a forecast to get a wide range of population from a small sample size.

Ideally, at the onset ,an inference is established based on whatever data is available. This inference is always correct based on the data set analysed. However, as the sample size keeps on increasing, the initial inference may or may not hold good. This is so because the succeeding samples may or may not show the similar behaviour in the given environment.

The succeeding sample sets may be infuenced by the results of the preceeding sample results and hence the variance may be low . This bahavoiur of the sample set (variable) is due to auto correlation of variable and the effect is called as bias.

Bias may be positive or negetive. In an unbiased sample, differences between the samples taken from a random variable and its true distribution, or differences between the samples of units from a population and the entire population they represent, should result only from chance. If their differences are not only due to chance, then there is a sampling bias. Sampling bias often arises because certain values of the variable are systematically under-represented or over-represented with respect to the true distribution of the variable (like in our opinion poll example above). Because of its consistent nature, sampling bias leads to a systematic distortion of the estimate of the sampled probability distribution. This distortion cannot be eliminated by increasing the number of data samples and must be corrected for by means of appropriate techniques.

So what causes bias in samples /Sampling bias)?

A common cause of sampling bias lies in the design of the study or in the data collection procedure, both of which may favor or disfavor collecting data from certain classes or individuals or in certain conditions. Sampling bias is also particularly prominent whenever researchers adopt sampling strategies based on judgment or convenience, in which the criterion used to select samples is somehow related to the variables of interest.Fro example an academic researcher collecting opinion data may choose, because of convenience, to collect opinions mostly from college students because they happen to live nearby, and this will further bias the sampling toward the opinion prevalent in the social class living in the neighborhood.

In social and economic sciences, extracting random samples typically requires a sampling frame such as the list of the units of the whole population, or some auxiliary information on some key characteristics of the target population to be sampled. For instance, conducting a study about primary schools in a certain country requires obtaining a list of all schools in the country, from which a sample can be extracted. However, using a sampling frame does not necessarily prevent sampling bias. For example, one may fail to correctly determine the target population or use outdated and incomplete information, thereby excluding sections of the target population. Furthermore, even when the sampling frame is selected properly, sampling bias can arise from non-responsive sampling units (e.g. certain classes of subjects might be more likely to refuse to participate, or may be harder to contact etc.) Non-responses are particularly likely to cause bias whenever the reason of non-response is related to the phenomenon under study. Figure 1 illustrates how the mismatches between sampling frame and target population, as well as non-responses, could bias the sample.

In experiments in physical and biological sciences, sampling bias often occurs when the target variable to be measured during the experiment (e.g. the energy of a physical system) is correlated to other factors (e.g. the temperature of the system) that are kept fixed or confined within a controlled range during the experiment. Consider for example the determination of the probability distribution of the speed of all cars on British roads at any time during a certain day. Speed is definitely related to location: therefore measuring speed only at certain types of locations may bias the sample. For instance, if all measures are taken at busy traffic junctions in the city centre, the sampled distribution of car speeds will not be representative of Britain’s cars and will be strongly biased toward slow speeds, because it neglects cars travelling on motorways and on other fast roads. It is important to note that a systematic distortion of a sampled distribution of a random variable can result also from factors other than sampling bias, such as a systematic error in the instruments used to collect the sample data. Considering again the example of the distribution of the speed of cars in Britain, and suppose that the experimenter has access to the simultaneous reading of the speedometers placed on every car, so that there is no sampling bias. If most speedometers are tuned to overestimate the speed, and to overestimate it more at higher speed, then the resulting sampled distribution will be biased toward high velocities.

and the remedies ?

To reduce sampling bias, the two most important steps when designing a study or an experiment are

(i) to avoid judgment or convenience sampling

(ii) to ensure that the target population is properly defined and that the sample frame matches it as much as possible.

When finite resources or efficiency reasons limit the possibility to sample the entire population, care should be taken to ensure that the excluded populations do not differ from the overall one in terms of the statistics to be measured. In social sciences population representative surveys most commonly are not simple random samples, but follow more complex sample designs. For instance, in a typical household survey a sample of households is selected in two stages: in a first stage there is a selection of villages or parts of cities (cluster) and in a second stage a set number of households is selected within the same cluster. When adopting such complex sample designs it is essential to ensure that the sample frame information is used properly and that the probability and random selection are implemented and documented at each stage of the sampling process. In fact, such information will be essential to compute unbiased estimates for the population using sampling weights (the inverse of the probability of selection) and taking into account the sampling design in order to properly compute the sampling error. In complex sample designs the sampling error will always be larger than in the simple random samples (Cochran 1977).

Whenever the sampling frame includes units that do not exist anymore (e.g., because the sample frames are incorrect and outdated) it will be impossible to obtain any samples from such non existing units. This situation does not bias the estimates, provided that such cases are not substituted using non-random methods, and that original sampling weights are properly adjusted to take into account such sample frame imperfections (nevertheless sample frame imperfections clearly have costs implications and if the sample size is reduced this also influences the size of the sampling error).

Solutions to the bias due to non-response are much more articulated, and can generally be divided in ex-ante and ex-post solutions (Groves et al. 1998). Ex-ante solutions try to prevent and minimize non-response in various ways (for instance specific training of enumerators, several attempts to interview the respondent, etc.) whereas ex-post solutions try to gather auxiliary information about non-respondents which is then used to calculate a probability of response for different population sub-groups and so re-weight response data for the inverse of such probability or alternatively some post-stratification and calibration.

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