# How do you increase p-value?

## How do you increase p-value?

Here is a list of the top 7 tricks that can be used to get statistically significant p-values:

- Using multiple testing.
- Increasing the sample size.
- Handling missing values in the way that benefits you the most.
- Adding/removing other variables from the model.
- Trying different statistical tests.
- Categorizing numeric variables.

**What is considered a high p-value?**

A small p-value (typically ≤ 0.05) indicates strong evidence against the null hypothesis, so you reject the null hypothesis. A large p-value (> 0.05) indicates weak evidence against the null hypothesis, so you fail to reject the null hypothesis.

### What causes p-value to increase?

The p-values is affected by the sample size. Larger the sample size, smaller is the p-values. Increasing the sample size will tend to result in a smaller P-value only if the null hypothesis is false.

**What does p-value of 0.01 mean?**

eg the p-value = 0.01, it means if you reproduced the experiment (with the same conditions) 100 times, and assuming the null hypothesis is true, you would see the results only 1 time. OR in the case that the null hypothesis is true, there’s only a 1% chance of seeing the results.

#### How do you get a low p-value?

Spread of the data. The spread of observations in a data set is measured commonly with standard deviation. The bigger the standard deviation, the more the spread of observations and the lower the P value.

**Is higher p-value better?**

The smaller the p-value, the stronger the evidence that you should reject the null hypothesis. A p-value less than 0.05 (typically ≤ 0.05) is statistically significant. A p-value higher than 0.05 (> 0.05) is not statistically significant and indicates strong evidence for the null hypothesis.

## Is 0.07 statistically significant?

at the margin of statistical significance (p<0.07) close to being statistically signiﬁcant (p=0.055) only slightly non-significant (p=0.0738) provisionally significant (p=0.073)

**What happens when you increase significance level?**

Use a higher significance level (also called alpha or α). Using a higher significance level increases the probability that you reject the null hypothesis. However, be cautious, because you do not want to reject a null hypothesis that is actually true. (Rejecting a null hypothesis that is true is called type I error.)