Making inferences involves using data from a sample to draw conclusions or make predictions about a population. There is always some degree of uncertainty when the results from a study are used to make inferences. However, increasing the size of the sample reduces uncertainty.
Below, the recorded lengths of $100$ irradiated and $100$ non-irradiated apple seeds are shown.

Length (cm)	Treatment group	Control group
$8-9.9$	$2$	$0$
$10-11.9$	$10$	$0$
$12-13.9$	$14$	$4$
$14-15.9$	$18$	$14$
$16-17.9$	$28$	$31$
$18-19.9$	$18$	$28$
$20-21.9$	$6$	$20$
$22-23.9$	$4$	$3$

The average length of the seedlings in each group can be found. Thus, seedlings from irradiated seeds can be assumed to grow, on average, about $2$ cm less when exposed to radiation. Another way to study the effects of the treatment is to present the data sets in histograms.

When the histograms are shown together, it's possible to see that the spread of the data of the treatment group is larger than that of the control group. This could mean radiation exposure stunts growth. Because the sample size in this experiment is relatively large, one can claim that the results are reliable.