The P-value means probability value and also known as the significance value.
In SPSS research p-value is a measure of how much evidence we have against the null hypothesis.

When we compute a statistics (e.g. F-statistics, Z-statistics, T- statistics) we compare it with critical value from the statistics table with the consideration of the sample size and the degree of freedoms. In SPSS we use the significant value instead.

Let me introduce the α-value which represent the part of data not covered (error) usually given by α = (1-Confidence Interval)
Confidence Interval is a measure of how much (%) of your dataset is used
In SPSS research we either use 99%, 95% or 90% at worse Confidence Interval. This implies in other words the level of confidence is 0.01, 0.05 and 0.10 respectively.

Most SPSS researchers use 95% Confidence Interval in most analysis tests. For this particular case our α is 0.05. In this case if we carry out a statistical test and our significance value P < 0.05 we reject the null hypothesis indicating there is significant difference between the means. If the significance value P ≥ 0.05 we don’t have enough evidence to reject the null hypothesis in that, the difference between the means is not significant.
SPSS researchers should know that the word significant does not mean “important” but instead in statistics it means “Probably True”.

The P-value means probability value and also known as the significance value.

In SPSS research p-value is a measure of how much evidence we have against the null hypothesis.

When we compute a statistics (e.g. F-statistics, Z-statistics, T- statistics) we compare it with critical value from the statistics table with the consideration of the sample size and the degree of freedoms. In SPSS we use the significant value instead.

Let me introduce the α-value which represent the part of data not covered (error) usually given by α = (1-Confidence Interval)

Confidence Interval is a measure of how much (%) of your dataset is used

In SPSS research we either use 99%, 95% or 90% at worse Confidence Interval. This implies in other words the level of confidence is 0.01, 0.05 and 0.10 respectively.

Most SPSS researchers use 95% Confidence Interval in most analysis tests. For this particular case our α is 0.05. In this case if we carry out a statistical test and our significance value P < 0.05 we reject the null hypothesis indicating there is significant difference between the means. If the significance value P ≥ 0.05 we don’t have enough evidence to reject the null hypothesis in that, the difference between the means is not significant. SPSS researchers should know that the word significant does not mean “important” but instead in statistics it means “Probably True”.