What is the p-value in simple terms?

A p-value is the probability of getting a result at least as extreme as the one you observed, assuming there is no real effect in the population. A small p-value means your data would be surprising if nothing were going on, so you treat the effect as statistically significant. It does not tell you the probability that your hypothesis is true.

What does a 0.05 p-value mean?

A p-value of 0.05 means there is a five percent chance of seeing a result this extreme if the null hypothesis of no effect were true. It sits exactly at the conventional threshold, so it is usually treated as just significant. You should still report the effect size, because a significant result can still be small.

Is the p-value of 0.055 significant?

By the standard 0.05 threshold, a p-value of 0.055 is not significant, because it sits just above the cut-off. The honest approach is to describe it as borderline or suggestive rather than rounding it down or calling it significant. Report the exact value and let the effect size and confidence interval inform the discussion.

Is 0.05 or 0.01 p-value better?

Neither is universally better; they set different tolerances for a false positive. A threshold of 0.01 is stricter and reduces the chance of declaring an effect that is not real, which suits fields where a wrong claim is costly. A threshold of 0.05 is the common default and is more willing to flag effects. You should choose and state the threshold before you analyse the data.

Understanding p-values and significance

A p-value is the probability of seeing a result at least as extreme as yours if the null hypothesis were true, that is, if there were no real effect in the wider population. A small p-value means your sample result would be unlikely under that assumption, so you treat the effect as statistically significant. A p-value is not the probability that your hypothesis is true, and it is not a measure of how large the effect is; it only tells you how surprising the data are if nothing is going on.

What the threshold actually decides

The familiar cut-off of .05 is a convention, not a law of nature. It sets your tolerance for a false positive, the risk of declaring an effect that is not really there. Choosing alpha of .05 means you accept a one-in-twenty chance of that error before you ever see the data. Some fields demand a stricter .01 because the cost of a wrong claim is higher. The threshold is a decision rule you commit to in advance; moving it after you see the result is one of the surest ways to lose your supervisor's trust in your dissertation.

A p-value is a statement about the sample-to-population leap: it asks how likely your sample result would be if the true population effect were zero.

What a p-value does not tell you

The most common misreadings cost marks. A p-value of .04 does not mean there is a ninety-six percent chance your hypothesis is correct, and a p-value of .20 does not prove the null hypothesis is true; it only means you lack the evidence to reject it. Crucially, significance is not size. With a large enough sample, a trivial difference will cross the threshold, which is why a p-value must always be reported next to an effect size that shows how big the effect is. The p-value answers "is it likely real?", never "does it matter?".

Reporting the value honestly

Report the exact p-value rather than just "p < .05" wherever you can, because a value of .049 and a value of .003 carry very different weight even though both clear the threshold. Treat a result of .055 as what it is, just over the line, and discuss it as suggestive rather than dressing it up as significant. Pairing the p-value with a confidence interval for the effect gives the reader far more than a bare significance flag, and the formatting rules for all of these numbers live in how to report statistics in APA style.

p-value	How to read it
Below .01	Strong evidence against the null hypothesis
Below .05	Conventionally significant; report the effect size too
Around .05 to .06	Borderline; describe as suggestive, not significant
Above .10	No evidence to reject the null; not proof it is true

Where the p-value sits in your analysis

A p-value is the last step of an inferential test, not the whole story. It arrives only after you have chosen the right test, checked its assumptions, and computed a test statistic, which is the path traced in the move from descriptive to inferential statistics. Read in that context, the p-value becomes a disciplined yes-or-no on a single, pre-stated question rather than a magic number. Reported alongside its effect size and confidence interval, it gives your results chapter the honesty examiners reward.