Final answer:
The statement is False; probability samples are based on random selection rather than on the judgment of the researcher. This method allows for a more representative and generalizable conclusion about the larger population because every member has an equal chance of being included in the sample.
Step-by-step explanation:
Probability samples are not based on a sampling judgment by the researcher, so the statement is False. Probability sampling involves random selection, allowing each member of the population an equal chance of being included in the sample, which reduces sampling bias and makes the results more generalizable to the population. This is in contrast to non-probability sampling where the researcher uses their judgment to select participants, such as in convenience sampling or judgmental sampling.
The concept of probability sampling aligns with statistical principles, such as the Central Limit Theorem, which states that as sample size increases, the sampling distribution of the means approaches a normal distribution, regardless of the shape of the population distribution. This provides a foundation for making inferences about the population based on the sample data. It is also important to note that larger sample sizes tend to provide more accurate estimates of the population parameters than smaller ones.
However, it's important to acknowledge the practical challenges in obtaining a true random sample, considering factors like the rare events principle in hypothesis testing. Nonetheless, probability sampling stands as the more objective method, aiming to obtain a representative segment of the population without the influence of a researcher's subjective judgment.