4. Do you think that increasing the number and kinds of observations you make about something would affect any inferences you make about those observations? Why or why not?

The number of observations (sample size) is important in determining the reliability of statistical inferences. The kind of observations depends on what hypothesis is being tested. Different statistical techniques are required depending on whether the data is categorical or discrete or continuous and whether observations are quantitative or qualitative. Increasing the kinds of observations (multiple sets of data) may also add to the reliability of the test of the hypothesis.

Explanation:

In general, a larger sample size improves the reliability of any statistical inference, particularly when there is more variability (higher variance) in the data and when the expected effect is small. A larger sample reduces the chance of random error.

Where more than one variable is associated with output data, it is of course necessary to collect data of different kinds. For example, if your hypothesis is:

H: y = ax1 + bx2 + cx3 +

then you need to have observations of x1, x2, x3