The consideration of time duration with the FFT, assuming stationary signals (multiple tones but they are not changing with time), is Frequency Resolution; how well you can still distinguish between two closely spaced tones (at approximately the same power level). The longer duration in time, the tighter the achievable resolution. For a rectangular window (which means you are taking the FFT of a data set directly without any modification of the envelope of the signal), the frequency resolution is 1/T where T is the duration of your (non-zero padded) data in time. This ends up being 1 bin width (1 point) in frequency given the relationship between the sampling rate, the number of points in the FFT and the time duration of the signal. You can zero pad the signal (meaning add more zeros to make the length of the data set longer) which will result in more points in frequency, but this simply interpolates more frequency samples of the underlying Discrete Time Fourier Transform (DTFT) but does not in any way increase the resolution (which makes sense, you did not add any more data).
When you have closely spaced tones that are at significantly different power levels, then the dynamic range (ability to see both strong and weak tones simultaneously) is of concern and in this case we often window the data first, which serves to reduce spectral leakage (sidelobes from one tone burying another weaker tone), at the expense of frequency resolution: so you gain dynamic range but the frequency resolution is degraded as a result.
A third consideration related to this is scalloping loss: which is the variation in amplitude for signals that are located exactly on a frequency bin versus mid-way between bins, which is what Cendron is referring to in his answer. This isn't an actual "loss" since the energy is just being smeared to adjacent bins, but does indicate the variation that could exist when only one bin is considered.
This is further demonstrated in the plots below, showing the frequency response for each bin in an example 10 point FFT. The first one shows the case for a Rectangular window (where the frequency response is called the Dirichlet Kernel) and the second one for when a Blackman Window (Showing how the variation can be significantly reduced through the use of windowing).
The lines show the output magnitude of EACH bin, given a frequency anywhere along the horizontal axis; for example in the top plot, if you drew a vertical line at x = 4.5, consistent with an actual input signal at this frequency location, every single bin in the 10 point FFT will have an output level somewhere between -20 dB and -4 dB (approximately). Compare this to the case where the input signal is exactly on a bin center, for example a vertical line at x = 5, only that bin has a magnitude output as the response from all the other bins is 0. From this we see that the magnitude of any one bin will vary up to 3.92 dB, given a consistent amplitude input signal that changes only in frequency, but this is just from energy that is getting translated to the other bins as the frequency is changed.
The second plot shows the same result when a window is used (in this case a 10 point Blackman Window), specifically showing the loss due to windowing (as we have selectively reduced our input signal) but more importantly showing the decreased variation as we vary the input signal along the x-axis (as detailed in the third plot).
From these examples you also clearly see the trade-off in frequency resolution and dynamic range that was referred to earlier.
You can further reduce scalloping loss effects for applications when you are streaming FFT results over time on longer data sets by using overlap-add techniques. This is all explained in detail in a classic paper by fred harris, "On the Use of Windows for Harmonic Analysis with the Discrete Fourier Transform", found at this link: http://web.mit.edu/xiphmont/Public/windows.pdf