Hello,
The concept of p-value is really important to understand clearly, and a lot of people are confused about it (even some professional statisticians!). I think what @the_doctor said is great, and I will just give some technical details.
When you compute a test statistic, this number comes from a random variable. Actually, a statistic is just a random variable. If you are not familiar with the field of probability yet and do not understand clearly what is a random variable, think of it as a box which produces numbers randomly (for example, a die can be seen as a random variable that can produce integers between 1 and 6).
In your example, you have a test statistic (let’s call it T) which corresponds to the mean difference of the two groups, and you are asking yourself the following question: given the assumption that “participants who consumed the weight loss pills lost the same amount of weight as those who didn’t take the pill”, what is the probability of observing the event |T| \geq 2.52, i.e. how likely it is to observe this result (or even worse) under the assumption we made before ? This assumption is called the null-hypothesis, and let’s denote it as H_0. In terms of conditional probabilities, we have \text{p-value} = \mathbb{P}_{H_0}(|T| \geq 2.52).
To calculate this p-value, we need to know the probability distribution of the statistic T under the null-hypothesis H_0. Since we cannot directly get this distribution by rigorous calculus (because we do not have enough information in our example), we need to estimate this distribution with a histogram.
The null-hypothesis H_0 can be reformulated as follows: “the pills have no effect on weight loss”. Hence, we can estimate the distribution of T under H_0 by randomly splitting our data values into two groups (and repeating this experiment a certain number of times to get a sampling distribution); indeed, when doing so, you are likely to observe no difference in weight loss between the two groups you created, because in each of your groups, you do not know who took the pills and who did not. In other words:
- if you know that the pills have no effect on weight loss, you will not observe any significant difference between the two groups you formed,
- if you know that the pills actually have an effect on weight loss, you will neither observe any significant difference between the two groups because you created them randomly, so in each group there should be the same proportion of people belonging to group A and group B, which will have a compensation effect.
Once we do that, we can get a histogram: it is an estimation of the probability distribution of T under the null-hypothesis H_0. We can then read the (estimated) probability of |T| being greater than 2.52, which corresponds to the p-value.
If this value is “small”, it means that under the null-hypothesis H_0, it is unlikely to observe that |T| \geq 2.52. In other words, under the assumption that the pills have no effect on weight loss, it is unlikely to observe this event, and that is why we reject the null-hypothesis and conclude that there is some evidence that the weight loss pill does affect the amount of weight people lost.
If this value is “big”, it means that under the null-hypothesis, it is quite likely to observe that |T| \geq 2.52, and that is why we fail to reject the null hypothesis that there’s no difference in the mean amount of weight lost by participants in both groups, and conclude that the weight loss pill doesn’t seem to be effective in helping people lose weight.
Here, the terms “small” and “big” depend on a threshold that you have set for yourself before starting the experiment.
Also, it is important to understand that even if your p-value is small (i.e. under your threshold), it does not necessarily mean that the null-hypothesis is false. Do you see why? Because the p-value is a probability calculated under the assumption that the null-hypothesis is true. And one could ask “okay, but what if the null-hypothesis is in reality false?”. So be careful about what you conclude when using p-value! 
I hope it makes sense to you!
when we have ideal symmetric parametric statistics with gaussian dispersion.