We saw in Chapter 3 that the mean of a sample has a standard error, and a mean that departs by more than twice its standard error from the population mean would be expected by chance only in about 5% of samples. Likewise, the difference between the means of two samples has a standard error. We do not usually know the population mean, so we may suppose that the mean of one of our samples estimates it. The sample mean may happen to be identical with the population mean but it more probably lies somewhere above or below the population mean, and there is a 95% chance that it is within 1.96 standard errors of it.
Consider now the mean of the second sample. If the sample comes from the same population its mean will also have a 95% chance of lying within 196 standard errors of the population mean but if we do not know the population mean we have only the means of our samples to guide us. Therefore, if we want to know whether they are likely to have come from the same population, we ask whether they lie within a certain range, represented by their standard errors, of each other.
Large sample standard error of difference between means
If SD1 represents the standard deviation of sample 1 and SD2 the standard deviation of sample 2, n1 the number in sample 1 and n2 the number in sample 2, the formula denoting the standard error of the difference between two means is:
The computation is straightforward.
Square the standard deviation of sample 1 and divide by the number of observations in the sample:(1)
Square the standard deviation of sample 2 and divide by the number of observations in the sample:(2)
Add (1) and (2).
Take the square root, to give equation 5.1. This is the standard error of the difference between the two means.
Large sample confidence interval for the difference in two means
From the data in the general practitioner wants to compare the mean of the printers’ blood pressures with the mean of the farmers’ blood pressures. The figures are set out first as in table 5.1 (which repeats table 3.1 ).
Analysing these figures in accordance with the formula given above, we have:
The difference between the means is 88 – 79 = 9 mmHg.
For large samples we can calculate a 95% confidence interval for the difference in means as
9 – 1.96 x 0.81 to 9 + 1.96 x 0.81 which is 7.41 to 10.59 mmHg.
For a small sample we need to modify this procedure, as described in Chapter 7.
Null hypothesis and type I error
In comparing the mean blood pressures of the printers and the farmers we are testing the hypothesis that the two samples came from the same population of blood pressures. The hypothesis that there is no difference between the population from which the printers’ blood pressures were drawn and the population from which the farmers’ blood pressures were drawn is called the null hypothesis.
But what do we mean by “no difference”? Chance alone will almost certainly ensure that there is some difference between the sample means, for they are most unlikely to be identical. Consequently we set limits within which we shall regard the samples as not having any significant difference. If we set the limits at twice the standard error of the difference, and regard a mean outside this range as coming from another population, we shall on average be wrong about one time in 20 if the null hypothesis is in fact true. If we do obtain a mean difference bigger than two standard errors we are faced with two choices: either an unusual event has happened, or the null hypothesis is incorrect. Imagine tossing a coin five times and getting the same face each time. This has nearly the same probability (6.3%) as obtaining a mean difference bigger than two standard errors when the null hypothesis is true. Do we regard it as a lucky event or suspect a biased coin? If we are unwilling to believe in unlucky events, we reject the null hypothesis, in this case that the coin is a fair one.
To reject the null hypothesis when it is true is to make what is known as a type I error . The level at which a result is declared significant is known as the type I error rate, often denoted by α. We try to show that a null hypothesis is unlikely , not its converse (that it is likely), so a difference which is greater than the limits we have set, and which we therefore regard as “significant”, makes the null hypothesis unlikely . However, a difference within the limits we have set, and which we therefore regard as “non-significant”, does not make the hypothesis likely.
A range of not more than two standard errors is often taken as implying “no difference” but there is nothing to stop investigators choosing a range of three standard errors (or more) if they want to reduce the chances of a type I error.
Testing for differences of two means
To find out whether the difference in blood pressure of printers and farmers could have arisen by chance the general practitioner erects the null hypothesis that there is no significant difference between them. The question is, how many multiples of its standard error does the difference in means difference represent? Since the difference in means is 9 mmHg and its standard error is 0.81 mmHg, the answer is: 9/0.81 = 11.1. We usually denote the ratio of an estimate to its standard error by “z”, that is, z = 11.1. Reference to Table A (Appendix table A.pdf) shows that z is far beyond the figure of 3.291 standard deviations, representing a probability of 0.001 (or 1 in 1000). The probability of a difference of 11.1 standard errors or more occurring by chance is therefore exceedingly low, and correspondingly the null hypothesis that these two samples came from the same population of observations is exceedingly unlikely. The probability is known as the P value and may be written P < 0.001.
It is worth recapping this procedure, which is at the heart of statistical inference. Suppose that we have samples from two groups of subjects, and we wish to see if they could plausibly come from the same population. The first approach would be to calculate the difference between two statistics (such as the means of the two groups) and calculate the 95% confidence interval. If the two samples were from the same population we would expect the confidence interval to include zero 95% of the time, and so if the confidence interval excludes zero we suspect that they are from a different population. The other approach is to compute the probability of getting the observed value, or one that is more extreme , if the null hypothesis were correct. This is the P value. If this is less than a specified level (usually 5%) then the result is declared significant and the null hypothesis is rejected. These two approaches, the estimation and hypothesis testing approach, are complementary. Imagine if the 95% confidence interval just captured the value zero, what would be the P value? A moment’s thought should convince one that it is 2.5%. This is known as a one sided P value , because it is the probability of getting the observed result or one bigger than it. However, the 95% confidence interval is two sided, because it excludes not only the 2.5% above the upper limit but also the 2.5% below the lower limit. To support the complementarity of the confidence interval approach and the null hypothesis testing approach, most authorities double the one sided P value to obtain a two sided P value (see below for the distinction between one sided and two sided tests).
Sometimes an investigator knows a mean from a very large number of observations and wants to compare the mean of her sample with it. We may not know the standard deviation of the large number of observations or the standard error of their mean but this need not hinder the comparison if we can assume that the standard error of the mean of the large number of observations is near zero or at least very small in relation to the standard error of the mean of the small sample.
This is because in equation 5.1 for calculating the standard error of the difference between the two means, when n1 is very large then becomes so small as to be negligible. The formula thus reduces to
which is the same as that for standard error of the sample mean, namely
Consequently we find the standard error of the mean of the sample and divide it into the difference between the means.
For example, a large number of observations has shown that the mean count of erythrocytes in men is In a sample of 100 men a mean count of 5.35 was found with standard deviation 1.1. The standard error of this mean is ,. The difference between the two means is 5.5 – 5.35 = 0.15. This difference, divided by the standard error, gives z = 0.15/0.11 = 136. This figure is well below the 5% level of 1.96 and in fact is below the 10% level of 1.645 (see table A ). We therefore conclude that the difference could have arisen by chance.
Alternative hypothesis and type II error
It is important to realise that when we are comparing two groups a non-significant result does not mean that we have proved the two samples come from the same population – it simply means that we have failed to prove that they do not come from the population. When planning studies it is useful to think of what differences are likely to arise between the two groups, or what would be clinically worthwhile; for example, what do we expect to be the improved benefit from a new treatment in a clinical trial? This leads to a study hypothesis , which is a difference we would like to demonstrate. To contrast the study hypothesis with the null hypothesis, it is often called the alternative hypothesis . If we do not reject the null hypothesis when in fact there is a difference between the groups we make what is known as a type II error . The type II error rate is often denoted as . The power of a study is defined as 1 – and is the probability of rejecting the null hypothesis when it is false. The most common reason for type II errors is that the study is too small.
The concept of power is really only relevant when a study is being planned (see Chapter 13 for sample size calculations). After a study has been completed, we wish to make statements not about hypothetical alternative hypotheses but about the data, and the way to do this is with estimates and confidence intervals.(1)
Why is the P value not the probability that the null hypothesis is true?
A moment’s reflection should convince you that the P value could not be the probability that the null hypothesis is true. Suppose we got exactly the same value for the mean in two samples (if the samples were small and the observations coarsely rounded this would not be uncommon; the difference between the means is zero). The probability of getting the observed result (zero) or a result more extreme (a result that is either positive or negative) is unity, that is we can be certain that we must obtain a result which is positive, negative or zero. However, we can never be certain that the null hypothesis is true, especially with small samples, so clearly the statement that the P value is the probability that the null hypothesis is true is in error. We can think of it as a measure of the strength of evidence against the null hypothesis, but since it is critically dependent on the sample size we should not compare P values to argue that a difference found in one group is more “significant” than a difference found in another.
Gardner MJ Altman DG, editors. Statistics with Confidence . London: BMJ Publishing Group. Differences between means: type I and type II errors and power
5.1 In one group of 62 patients with iron deficiency anaemia the haemoglobin level was 1 2.2 g/dl, standard deviation 1.8 g/dl; in another group of 35 patients it was 10.9 g/dl, standard deviation 2.1 g/dl.
Answers chapter 5 Q1.pdf
What is the standard error of the difference between the two means, and what is the significance of the difference? What is the difference? Give an approximate 95% confidence interval for the difference. 5.2 If the mean haemoglobin level in the general population is taken as 14.4 g/dl, what is the standard error of the difference between the mean of the first sample and the population mean and what is the significance of this difference?
Answers chapter 5 Q2.pdf
What is the difference between Type 1 and Type 2 error power? ›
What are Type I and Type II errors? In statistics, a Type I error means rejecting the null hypothesis when it's actually true, while a Type II error means failing to reject the null hypothesis when it's actually false.How is power related to type I and type II errors? ›
Simply put, power is the probability of not making a Type II error, according to Neil Weiss in Introductory Statistics. Mathematically, power is 1 – beta. The power of a hypothesis test is between 0 and 1; if the power is close to 1, the hypothesis test is very good at detecting a false null hypothesis.What is the difference between Type I error and Type II error quizlet? ›
A Type I error is committed when we reject a null hypothesis that is, in reality, true. A Type II error is committed when we fail to reject a null hypothesis that is, in reality, not true.What is the power of Type I error? ›
Traditionally, the type 1 error rate is limited using a significance level of 5%. Experiments are often designed for a power of 80% using power analysis. Note that it depends on the test whether it's possible to determine the statistical power.What is Type 1 error power? ›
A type I error occurs when in research when we reject the null hypothesis and erroneously state that the study found significant differences when there indeed was no difference. In other words, it is equivalent to saying that the groups or variables differ when, in fact, they do not or having false positives.How do you remember the difference between type1 and type 2 errors? ›
So here's the mnemonic: first, a Type I error can be viewed as a "false alarm" while a Type II error as a "missed detection"; second, note that the phrase "false alarm" has fewer letters than "missed detection," and analogously the numeral 1 (for Type I error) is smaller than 2 (for Type I error).What are examples of Type 2 errors? ›
A type II error produces a false negative, also known as an error of omission. For example, a test for a disease may report a negative result when the patient is infected. This is a type II error because we accept the conclusion of the test as negative, even though it is incorrect.What are the differences among Type I Type II and Type III error rates? ›
Type I error: "rejecting the null hypothesis when it is true". Type II error: "failing to reject the null hypothesis when it is false". Type III error: "correctly rejecting the null hypothesis for the wrong reason". (1948, p.What is the power of type 2 error? ›
The type II error is also known as a false negative. The type II error has an inverse relationship with the power of a statistical test. This means that the higher power of a statistical test, the lower the probability of committing a type II error.How do you get a Type 2 error from power? ›
How to Calculate the Probability of a Type II Error for a Specific Significance Test when Given the Power. Step 1: Identify the given power value. Step 2: Use the formula 1 - Power = P(Type II Error) to calculate the probability of the Type II Error.
What effect does effect size have on type II errors and power? ›
This type of error is termed Type II error. Like statistical significance, statistical power depends upon effect size and sample size. If the effect size of the intervention is large, it is possible to detect such an effect in smaller sample numbers, whereas a smaller effect size would require larger sample sizes.Why is it important to know the difference between type I and type II errors? ›
Specifically, they can make either Type I or Type II errors. As you analyze your own data and test hypotheses, understanding the difference between Type I and Type II errors is extremely important, because there's a risk of making each type of error in every analysis, and the amount of risk is in your control.Are Type 1 and Type 2 errors opposite? ›
Type I and Type II errors are inversely related: As one increases, the other decreases. The Type I, or α (alpha), error rate is usually set in advance by the researcher.What is a Type 2 error quizlet? ›
type II error. An error that occurs when a researcher concludes that the independent variable had no effect on the dependent variable, when in truth it did; a "false negative" type II error. occurs when researchers fail to reject a false null hypotheses.What is a Type 2 error in statistics? ›
In statistical hypothesis testing, a type I error is the mistaken rejection of an actually true null hypothesis (also known as a "false positive" finding or conclusion; example: "an innocent person is convicted"), while a type II error is the failure to reject a null hypothesis that is actually false (also known as a " ...What is an example of a type I error? ›
For example, a type I error would convict someone of a crime when they are actually innocent. A type II error would acquit a guilty individual when they are guilty of a crime.Does power decrease with Type 1 error? ›
From the relationship between the probability of a Type I and a Type II error (as α (alpha) decreases, β (beta) increases), we can see that as α (alpha) decreases, Power = 1 – β = 1 – beta also decreases.What are 5 types of errors? ›
- The error may arise from the different source and are usually classified into the following types. ...
- Gross Errors.
- Systematic Errors.
- Random Errors.
- Gross Errors.
Type II error is mainly caused by the statistical power of a test being low. A Type II error will occur if the statistical test is not powerful enough. The size of the sample can also lead to a Type I error because the outcome of the test will be affected.Which of the following is true about Type 1 and type 2 errors? ›
The correct option is (c) Type I and Type II error probabilities are conditional probabilities.
Is a type 2 error a random error? ›
A Type II error occurs when there really is a difference (association, correlation) overall, but random sampling caused your data to not show a statistically significant difference.What are the 3 types of errors? ›
- (1) Systematic errors. With this type of error, the measured value is biased due to a specific cause. ...
- (2) Random errors. This type of error is caused by random circumstances during the measurement process.
- (3) Negligent errors.
Data can be affected by two types of error: sampling error and non-sampling error.What is a Type 2 error in psychology? ›
A type II error Is a false negative. It is where you accept the null hypothesis when it is false (e.g. you think the building is not on fire, and stay inside, but it is burning).What four factors affect the power of a test? ›
The 4 primary factors that affect the power of a statistical test are a level, difference between group means, variability among subjects, and sample size.How do you find power in statistics? ›
Statistical power is the probability that a hypothesis test correctly infers that a sample effect exists in the population. In other words, the test correctly rejects a false null hypothesis. Consequently, power is inversely related to a Type II error. Power = 1 – β.What is the relationship between power and Type 2 error rate? ›
The power of a test is the probability that the test will reject the null hypothesis when the alternative hypothesis is true. In other words, the probability of not making a Type II error.What affects Type 1 error? ›
What causes type 1 errors? Type 1 errors can result from two sources: random chance and improper research techniques. Random chance: no random sample, whether it's a pre-election poll or an A/B test, can ever perfectly represent the population it intends to describe.Does power increase with effect size? ›
Large effect sizes increase statistical power and decrease the needed sample size. Measuring for large effect sizes is a great decision made by researchers. Large effect sizes can be detected with smaller sample sizes and always lead to increased statistical power.What effect does sample size have on type I and type II errors? ›
Having a larger sample size does not increase or decrease the Type I error, assuming a constant alpha level. However, the likelihood of a Type II error decreases as sample size increases, all other things being equal (i.e., the alpha level and the size of the true population effect).
What is meant by the power of a test? ›
Power is the probability that a test of significance will pick up on an effect that is present. Power is the probability that a test of significance will detect a deviation from the null hypothesis, should such a deviation exist. Power is the probability of avoiding a Type II error.What are two ways to increase the power of a test? ›
The power of a test can be increased in a number of ways, for example increasing the sample size, decreasing the standard error, increasing the difference between the sample statistic and the hypothesized parameter, or increasing the alpha level.What is Type 1 error and Type 2 error in machine learning? ›
Type I and Type II errors are very common in machine learning and statistics. Type I error occurs when the Null Hypothesis (H0) is mistakenly rejected. This is also referred to as the False Positive Error. Type II error occurs when a Null Hypothesis that is actually false is accepted.What is the symbol for Type II error? ›
A Type II error (sometimes called a Type 2 error) is the failure to reject a false null hypothesis. The probability of a type II error is denoted by the beta symbol β.Why is Type 2 error severe? ›
But if you can see then Type 2 error is also dangerous because freeing a guilty can bring more chaos in societies because now the guilty can do more harm to society.What is a Type I error quizlet? ›
A type I error occurs when we reject the null, but we should not have. In other words, you have found an effect that does not exist.What is a type II error multiple choice? ›
A Type II error occurs when. a null hypothesis is rejected but should not be rejected.What is the power of a Type 2 error? ›
The type II error is also known as a false negative. The type II error has an inverse relationship with the power of a statistical test. This means that the higher power of a statistical test, the lower the probability of committing a type II error.Does low power increase Type 2 error? ›
A type II error is commonly caused if the statistical power of a test is too low. The highest the statistical power, the greater the chance of avoiding an error. It's often recommended that the statistical power should be set to at least 80% prior to conducting any testing.What happens to power as Type 1 error increases? ›
Graphical depiction of the relation between Type I and Type II errors, and the power of the test. Type I and Type II errors are inversely related: As one increases, the other decreases.
What causes Type 1 errors? ›
What causes type 1 errors? Type 1 errors can result from two sources: random chance and improper research techniques. Random chance: no random sample, whether it's a pre-election poll or an A/B test, can ever perfectly represent the population it intends to describe.What does power of a study mean? ›
The power of a study, pβ, is the probability that the study will detect a predetermined difference in measurement between the two groups, if it truly exists, given a pre-set value of pα and a sample size, N.How much statistical power is enough? ›
Scientists are usually satisfied when the statistical power is 0.8 or higher, corresponding to an 80% chance of concluding there's a real effect.What is the relationship between power and type 2 error rate? ›
The power of a test is the probability that the test will reject the null hypothesis when the alternative hypothesis is true. In other words, the probability of not making a Type II error.Why is type 2 error worse? ›
On the other hand, a Type II error occurs when the alternative hypothesis is true and we do not reject the null hypothesis. In such a way our test incorrectly provides evidence against the alternative hypothesis. Thus a Type II error can be thought of as a “false negative” test result.What are the differences among type I Type II and Type III error rates? ›
Type I error: "rejecting the null hypothesis when it is true". Type II error: "failing to reject the null hypothesis when it is false". Type III error: "correctly rejecting the null hypothesis for the wrong reason". (1948, p.