Had you done this test one-tailed, however, the p you would have got would be half of the two-tailed value (0.03). Also, if you do a two-tailed test and find that your p is 0.06, then you would conclude that your results were not significant (because 0.06 is bigger than the critical value of 0.05). If you don’t do this, then you have done a two-tailed test using a different level of significance from the one you set out to use, which is cheating therefore, one-tailed tests temp people to cheat. If you do a one-tailed test and the results turn out to be in the opposite direction to what you predicted you must ignore them and accept the null hypothesis.

What are the arguments for not using one-tailed tests? The significance of a test statistic is directly linked to the sample size: the same effect will have different p-values in different-sized samples: small differences can be deemed ‘significant’ in large samples, and large effects might be deemed ‘non-significant’ in small samples. What effect does sample size have on statistical significance?

It can be influenced by the intentions of the scientist.

Significance (the p-value) is affected by the sample size.

It doesn’t allow you to draw any firm conclusions about the null hypotheses.What are the major problems with null hypothesis significance testing? A Type II error is the opposite: when you believe that there is no effect in the population when, in reality, there is. What is the difference between a Type I and Type II error?Ī Type I error is when you believe that there is a genuine effect in the population, when in fact there is not. In this example, therefore, because the overlap of 1.5 is greater than 0.88 (the overlap for p = 0.05), it suggests that there is more overlap than you would get for p < 0.05 therefore, the oxytocin and non-oxytocin groups are unlikely to be significantly different from each other using an α-level of 0.05.

The moderate overlap (0.88) tells us the maximum amount of overlap that would yield a significant difference (assuming a 0.05 Type I error rate) therefore, an overlap less than this indicates that p would be less than 0.05 (i.e., a difference deemed more significant than p < 0.05), whereas an overlap greater than this value indicates that p is greater than 0.05 (which is typically interpreted as non-significant). We can calculate the size of the overlap of the confidence intervals by subtracting the lower limit of the confidence interval for the oxytocin group (6) from the upper limit of the confidence interval for the non-oxytocin group (7.5), which gives us 1.5. Moderate overlap, which would indicate a significant difference between groups at p = 0.05, would be half of this value (i.e., 0.88). For the non-oxytocin group, it ranges from 4.5 to 7.5, which is a length of 3 and an MOE of 1.5. The confidence interval for the oxytocin group ranges from 6 to 10 so has a length of 4 and an MOE of half this value (i.e., 2). What is the value of the MOE? Are trust levels likely to be significantly different in the two groups using an α-level of 0.05? In a study based on # Puzzle 1, trust ratings were 8, 95% CI, in the oxytocin group and 6, 95%CI, in the non-oxytocin group. Null hypothesis: People’s ratings of trust towards strangers are identical regardless of whether they were exposed to oxytocin or not.Īlternative hypothesis: Compared to people who are not exposed to oxytocin, people who are exposed to oxytocin will give a higher rating of trust towards strangers. What are the null and alternative hypotheses? They rate how much they trusted the stranger from 0 (no trust) to 10 (complete trust). You do an experiment in which groups of people are exposed to oxytocin or not while meeting a stranger. According to Paul Zak, the hormone oxytocin increases trust in humans.

Ceebot chapter 8 puzzle 6 solution