## To determine the level of confidence or how sure you can be of your results, you need to consider both sampling error and non-sampling error.

Sampling error refers to the variability in your sample that arises due to chance. In your case, with a repeated measures design and 5 subjects, the sample size is quite small. The smaller the sample size, the larger the sampling error and the less reliable your results may be. To get an idea of the sampling error, you can calculate the standard error of the mean (SEM) for your data. The SEM represents the average amount of variability you would expect across repeated samples of the same size. The smaller the SEM, the more precise your estimate of the population mean.

Non-sampling error, on the other hand, refers to other factors that can affect your results beyond chance. In a repeated measures design, one potential source of non-sampling error is the order of conditions (in this case, sunny and cloudy days) and its potential effect on mood ratings. If the order of conditions had an effect on mood, it could confound your results, making it difficult to attribute the observed differences solely to the weather conditions. To evaluate the potential impact of non-sampling error, you can consider conducting a counterbalancing procedure, where you randomize the order of conditions across participants to minimize any order effects.

Additionally, you should consider other factors that could influence mood ratings, such as time of day, personal factors, or any restrictions on certain weather conditions. Controlling for these factors can help increase the internal validity of your study.

Finally, it's important to calculate the actual level of significance based on your data rather than relying on personal estimates. This can be done by conducting a statistical test, such as a repeated measures analysis of variance (ANOVA), and calculating the p-value associated with the observed effect. A p-value represents the probability of obtaining the observed effect (or a more extreme effect) under the assumption that the null hypothesis (no effect of weather) is true. The smaller the p-value, the stronger the evidence against the null hypothesis and the more confident you can be in your results.

In summary, to determine how sure you can be of your results, you should consider the sampling error by calculating the standard error of the mean, address potential non-sampling error by controlling for confounding factors and order effects, and calculate the actual level of significance using a statistical test.