A skewed return distribution has return a higher probability of returns on either the downside (negative skewness) or the upside (positive skewness). The bell-shaped normal distribution, which is not descriptive of actual return outcomes, has no skewness: it is symmetrical around the mean.
So, in conducting a fundamental analysis into likely outcomes, ask yourself about skewness. With outcomes are in the tails, ask whether the right tail is more likely than the left tail. That is, look for stocks with relatively high chance of the upside with a lower chance of an extreme downside.
However, be warned: You are playing against other investors. If the market is efficient and all investors are chasing positive skewness, then they will increase the price to yield a lower return. Indeed, theory informs that, in an efficient market, positive skewness yields lower returns. And it can be the case that investors get too enthusiastic about positive skewness, pushing the price too high. Perhaps the rush to AI stocks in 2024 was an example. Startups with new technology which often fail are another.
Thus, the value investor must look for skewness that the market does not see. Preference for positive skewness shows up in a high P/E ratio where price anticipates the high payoffs but maybe is overpriced, not discounted enough for the downside. High P/E has typically earned lower returns. But not always and that is for the investor to find out.