Final answer:
The totality of all observations is not a sample but rather the population. A sample is a subset of observations from the population. Large sample sizes are preferable in experiments, and non-supportive data or alternative investigation methods are still valuable in science.
Step-by-step explanation:
The totality of all observations is not a sample; it is the population. A sample is a subset of observations from the entire population used to infer and make generalizations about the population. In statistical terms, when we are talking about the totality of all observations, we are referring to every single observation that can be made about a particular group or situation which is known as the population.
In the context of hypothesis testing, scientists first propose a hypothesis about the world. They then make predictions about what observations would be expected if the hypothesis were true. After conducting experiments or observations, they compare the actual data collected with the expected data. If the data fit the theoretical distribution, it may suggest that the hypothesis is accurate. Otherwise, the hypothesis may be disproven or need modification.
Regarding experimentation, it's worth noting that not all experiments can be ethically or practically carried out on humans due to potential risks or ethical concerns. However, experiments can be conducted on human subjects with proper ethical considerations and approvals. Finally, large sample sizes in scientific experiments are indeed generally better as they tend to more accurately represent the population, and according to the central limit theorem, help the sampling distribution approach a normal distribution.
It is also important to remember that data which does not support a hypothesis is still useful. It contributes to the understanding of the situation at hand, and may help in refining or rejecting a hypothesis. Moreover, experimentation is just one type of scientific investigation; observational studies, case studies, and other methods also play vital roles in scientific inquiry.