As a scientist, it is your responsibility to think through why you got the results you did. This "why" is connected to the experiment's independent variable, but it is often also associated with external factors that varied between trials. These factors are, in essence, "controlled" variables that were not controlled. Here are some common external factors that affect the reliability of data gathered during an experiment:
Say you flip a coin once, and it lands on heads. Does this mean that, every time you flip a coin, it will land on heads? What if you do it twice, or three times? Each time you flip it, you are gathering new data that will (theoretically) eventually show that there is a 50/50 chance of the coin landing on heads or tails. However, the fewer trials you do, the more likely your result is going to be farther from that perfect 50/50 split.
A similar concept applies to your research. The more trials you do, the less likely it is for those "uncontrolled" factors to significantly skew your data. A trial size of 5 is generally large enough to conduct a statistical analysis on, but larger trial sizes (of at least 10) are generally encouraged. The more, the better!
As long as your trial size is relatively small, be sure to discuss its impact on your results as a limitation to the experiment.
If your project takes place outside of a completely controlled system (like an incubator or vacuum chamber), consider how environmental factors affected your data. If an experiment involved wood, for example, air humidity may have affected its physical properties. Other components that may be relative to your experiment can include air pollutants, temperature, and pressure.
Humans are not perfect, and believe it or not, this can play into scientific results. Reflect on your protocol and what measurements may have variations because of how you took them. For example, if you used a stopwatch or timer, your reaction time (from when the timer went off to when you stopped the experiment) may lead to small variations among your data. Other examples of human error can occur when reading measurements from a ruler and pouring liquids (if you spill any of the contents, for example).
Many measurement tools come with noted "uncertainties." These can help you figure out how accurate the tool is! For example, using a big beaker means your result is likely less accurate than it would be if you used a smaller graduated cylinder.
Pouring liquids from one container to another, you may notice that small amounts of the contents stay stuck on the bottom or sides of the container. Some tools, like pipettes, actually account for this residue in their uncertainties.
It is important to understand the tools you use! This can help you pick the best ones for your experiments.