Terminologies may vary from from field to field. However, using terms defined in the comments below:
Is there any difference among the following terms or they are same?
No, all three are equivalent to 'systematic error'.
Can these errors be reduced when one increase the sample size?
No, increasing sample size reduces random error, not systematic error.
These terms are taken from the field of epidemiology, specifically from Rothman and colleagues discussion of error in chapters 9 and 10 of Modern Epidemiology.
The goal of an investigator is to provide an accurate estimate of some measure (e.g. mean, relative risk, hazard ratio, et cetera) within a population. An accurate estimate is one that is both valid and precise. A valid estimate will have a point estimate (eg. mean, relative risk, hazard ratio, et cetera) that is close to the true value in the population. A precise estimate will have narrow confidence levels around the point estimate. In addition, an estimate can be internally-valid, relative to the study population, and externally-valid, relative to a generalized population.
Departures from accuracy are caused by error. There are two main types of error: systemic error and random error.
Systemic error, often referred to as bias, results in estimates that are not valid. Systemic error includes error due to confounding, selection bias, and information bias. Confounding can generally be corrected for with techniques such as stratification or regression. Selection and information bias have traditionally been either ignored or only qualitatively assessed in analyses, probably due to unfamiliarity with appropriate bias analyses. However, methodologies for qunatitative bias analysis do exist (e.g. Lash TL and AK Fink (2003)).
Random error results in estimates that are not precise. Random error includes sampling error and random measurement error, among others. Methods to increase precision include increaseing study size, increasing study efficiency, and precision-optimizing statistical analyses such as pooling and regression.
To illustrate why increasing sample size does not decrease systematic error with the dartboard analogy (copied from this CV post):
No matter how many darts are thrown at the board, the point estimate is not going to shift towards the true bulls-eye when there is 'high bias'. Here 'bias' is equivalent to 'systematic error', and 'variance' is equivalent to 'random error'.