Chi-Square statistics are reported with degrees of freedom and sample size in parentheses, the Pearson chi-square value (rounded to two decimal places), and the significance level: The percentage of participants that were married did not differ by gender, X2(1, N = 90) = 0.89, p > . 05.

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## How do you report chi-square results in SPSS?

- Click on Analyze -> Descriptive Statistics -> Crosstabs.
- Drag and drop (at least) one variable into the Row(s) box, and (at least) one into the Column(s) box.
- Click on Statistics, and select Chi-square.
- Press Continue, and then OK to do the chi-square test.
- The result will appear in the SPSS output viewer.

## How do you write chi-squared?

For a Chi-square test, a **p-value that is less than or equal to your significance level** indicates there is sufficient evidence to conclude that the observed distribution is not the same as the expected distribution. You can conclude that a relationship exists between the categorical variables.

## How do you interpret the chi-square value?

If your chi-square calculated value is greater than the chi-square critical value, then you reject your null hypothesis. If your chi-square calculated value is less than the chi-square critical value, then you “fail to reject” your null hypothesis.

## How do you report nonsignificant results?

A more appropriate way to report non-significant results is to **report the observed differences (the effect size)** along with the p-value and then carefully highlight which results were predicted to be different.

## What does it mean if the p-value is not significant?

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.

## How do you report statistically significant results?

- Means: Always report the mean (average value) along with a measure of variability (standard deviation(s) or standard error of the mean ). …
- Frequencies: Frequency data should be summarized in the text with appropriate measures such as percentages, proportions, or ratios.

## Should I report only significant results?

Yes, **non-significant results are just as important as significant ones**. If you are publishing a paper in the open literature, you should definitely report statistically insignificant results the same way you report statistically significant results. Otherwise, you contribute to underreporting bias.

## Do you report effect size for non-significant results?

**The effect size is completely separate from the p-value and should be reported and interpreted as** such. Effect size = clinical significance = much more important than statistical significance. So yes, it should always be reported, even when p >0.05 because a high p-value may simply be due to a small sample size.

## What does a p-value of 0.5 mean?

Mathematical probabilities like p-values range from 0 (no chance) to 1 (absolute certainty). So 0.5 means **a 50 percent chance** and 0.05 means a 5 percent chance. In most sciences, results yield a p-value of. 05 are considered on the borderline of statistical significance.

## Why do we use a 0.05 level of significance?

The significance level, also denoted as alpha or α, is the probability of rejecting the null hypothesis when it is true. For example, a significance level of 0.05 indicates **a 5% risk of concluding that a difference exists when there is no actual difference**.

## What happens when the results are insignificant?

Often a non-**significant finding increases one’s confidence** that the null hypothesis is false. … The statistical analysis shows that a difference as large or larger than the one obtained in the experiment would occur 11% of the time even if there were no true differences between the treatments.

## What does an alpha value of 0.05 mean?

A value of alpha = 0.05 implies **that the null hypothesis is rejected 5 % of the time when it is in fact true**. The choice of alpha is somewhat arbitrary, although in practice values of 0.1, 0.05, and 0.01 are common.

## What if there is no significant difference?

Perhaps the two groups overlap too much, or there just aren’t enough people in the two groups to establish a significant difference; when the researcher fails to find a significant difference, only one conclusion is possible: “**all possibilities remain**.” In other words, failure to find a significant difference means …

## How do you report effect size?

- The direction of the effect if applicable (e.g., given a difference between two treatments A and B, indicate if the measured effect is A – B or B – A ).
- The type of point estimate reported (e.g., a sample mean difference)

## How do you explain effect size?

What is effect size? Effect size is **a quantitative measure of the magnitude of the experimental effect**. The larger the effect size the stronger the relationship between two variables. You can look at the effect size when comparing any two groups to see how substantially different they are.

## What does it mean to have a large effect size?

Effect size tells you how meaningful the relationship between variables or the difference between groups is. … A large effect size means that **a research finding has practical significance**, while a small effect size indicates limited practical applications.

## What does a large p-value mean?

A large p-value (> 0.05) indicates **weak evidence against the null hypothesis**, so you fail to reject the null hypothesis. p-values very close to the cutoff (0.05) are considered to be marginal (could go either way).

## Is a p-value of 0.001 significant?

Most authors refer to statistically significant as P < 0.05 and **statistically highly significant as P < 0.001** (less than one in a thousand chance of being wrong). The asterisk system avoids the woolly term “significant”.

## What does a p-value of 0.1 mean?

The smaller the p-value, the stronger the evidence for rejecting the H_{}. This leads to the guidelines of p < 0.001 indicating very strong evidence against H_{}, p < 0.01 strong evidence, p < 0.05 moderate evidence, p < 0.1 weak evidence or a trend, and p ≥ 0.1 indicating **insufficient evidence**[1].

## What does a p-value above 0.05 mean?

P > 0.05 is the probability that the null hypothesis is true. … A statistically significant test result (P ≤ 0.05) means that the test hypothesis is false or should be rejected. A P value greater than 0.05 means **that no effect was observed**.

## What does a 0.01 significance level mean?

Significance Levels. The significance level for a given hypothesis test is a value for which a P-value less than or equal to is considered statistically significant. Typical values for are 0.1, 0.05, and 0.01. These values correspond to **the probability of observing such an extreme value by chance**.

## What does an alpha level of .01 mean?

Because alpha corresponds to a probability, it can range from 0 to 1. In practice, 0.01, 0.05, and 0.1 are the most commonly used values for alpha, representing a **1%, 5%, and 10% chance of a Type I error occurring** (i.e. rejecting the null hypothesis when it is in fact correct).

## What does t test tell you?

The t-test tells you **how significant the differences between groups are**; In other words it lets you know if those differences (measured in means) could have happened by chance. … A t-test can tell you by comparing the means of the two groups and letting you know the probability of those results happening by chance.

## How do you calculate a 5% significance level?

To get α **subtract your confidence level from 1**. For example, if you want to be 95 percent confident that your analysis is correct, the alpha level would be 1 – . 95 = 5 percent, assuming you had a one-tailed test. For two-tailed tests, divide the alpha level by 2.

## What does it mean if data is not statistically significant?

The “layman’s” meaning of not statistically significant is **that the strength of relationship or magnitude of difference observed in your SAMPLE, would more likely NOT BE OBSERVED IN the POPULATION your sample purports to represent**.

## What does it mean if a finding is statistically significant?

A result of an experiment is said to have **statistical significance** or be statistically significant if it is likely not caused by chance for a given statistical significance level. … It also means that there is a 5% chance that you could be wrong.