Final answer:
The effect of having a sample that does not represent all segments of the population is referred to as selection bias, which can result in skewed conclusions about the population due to unequal chances of selection for all its members. Option b. is the correct answer.
Step-by-step explanation:
The effect of having a sample that does not represent all segments of the population is called selection bias.
Selection bias occurs when the method by which a sample is chosen causes the sample to not accurately reflect the population being studied. This can lead to incorrect conclusions about the population, as not all members have an equal chance of being selected. For example, if a survey only includes people available at a certain time of day, it would not represent those with different schedules who are not present.
Sampling errors like this can significantly affect the validity of statistical analysis and subsequent findings. To avoid selection bias, researchers should use random sampling methods where each individual in a population has an equal chance of being chosen. Otherwise, even with a large sample size, the results could be skewed due to the unrepresentativeness of the sample.