If you are adding the data to a new table, follow the remaining steps. The wizard prompts you to review the field properties. Click a column in the lower half of the page to display the corresponding field's properties.
Optionally, do any of the following:. Access reviews the first eight rows in each column to suggest the data type for the corresponding field. If the column in the worksheet contains different types of values, such as text and numbers, in the first eight rows of a column, the wizard suggests a data type that is compatible with all the values in the column — most often, the text data type. Although you can choose a different data type, remember that values that are incompatible with the data type that you choose will be either ignored or converted incorrectly during the import process.
For more information about how to correct missing or incorrect values, see the section Troubleshoot missing or incorrect values , later in this article. To completely skip a source column, select the Do not import field Skip check box. In the next screen, specify a primary key for the table. If you select Let Access add primary key , Access adds an AutoNumber field as the first field in the destination table, and automatically populates it with unique ID values, starting with 1.
Click Next. In the final wizard screen, specify a name for the destination table. In the Import to Table box, type a name for the table.
If the table already exists, Access displays a prompt that asks whether you want to overwrite the existing contents of the table. Click Yes to continue or No to specify a different name for the destination table, and then click Finish to import the data. If Access was able to import some or all the data, the wizard displays a page that shows you the status of the import operation.
In addition, you can save the details of the operation for future use as a specification. Conversely, if the operation completely failed, Access displays the message An error occurred trying to import file.
Click Yes to save the details of the operation for future use. Saving the details helps you repeat the operation at a later time without having to step through the wizard each time.
See Save the details of an import or export operation as a specification to learn how to save your save your specification details.
See Run a saved import or export specification to learn how to run your saved import or link specifications. See Schedule an import or export specification to learn how to schedule import and link tasks to run at specific times. If you receive the message An error occurred trying to import file , the import operation completely failed. Conversely, if the import operation displays a dialog box that prompts you to save the details of the operation, the operation was able to import all or some of the data.
The status message also mentions the name of the error log table that contains the description of any errors that occurred during the import operation.
Important: Even if the status message indicates a completely successful operation, you should review the contents and structure of the table to ensure that everything looks correct before you start using the table. The following table describes the steps that you can take to correct missing or incorrect values. Tip: While you are troubleshooting the results, if you find just a few missing values, you can add them to the table manually. Conversely, if you find that entire columns or a large number of values are either missing or were not imported properly, you should correct the problem in the source file.
After you have corrected all known problems, repeat the import operation. Graphical elements, such as logos, charts, and pictures cannot be imported.
Manually add them to the database after completing the import operation. The results of a calculated column or cells are imported, but not the underlying formula.
During the import operation, you can specify a data type that is compatible with the formula results, such as Number. However, if the source worksheet or range includes a column that contains only -1 or 0 values, Access, by default, creates a numeric field for the column.
When you import data to a new table or append data to an existing table, Access does not enable support for multiple values in a field, even if the source column contains a list of values separated by semicolon ;. The list of values is treated as a single value and is placed in a text field. If data appears truncated in a column in the Access table, try increasing the width of the column in Datasheet view. If that doesn't resolve the issue, the data in a numeric column in Excel is too large for the field size of the destination field in Access.
For example, the destination field might have the FieldSize property set to Byte in an Access database but the source data contains a value greater than Correct the values in the source file and try importing again. You might have to set the Format property of certain fields in design view to ensure that the values are displayed correctly in Datasheet view.
For example:. Long and medium dates might appear as short dates in Access. Note: If the source worksheet contains rich text formatting such as bold, underline, or italics, the text is imported, but the formatting is lost.
Records that you are importing might contain duplicate values that cannot be stored in the primary key field of the destination table or in a field that has the Indexed property set to Yes No Duplicates. Eliminate the duplicate values in the source file and try importing again. The date fields that are imported from an Excel worksheet might be off by four years.
Excel for Windows uses the Date System in which serial numbers range from 1 to 65, , which correspond to the dates January 1, through December 31, However, Excel for the Macintosh uses the Date System in which serial numbers range from 0 to 63, , which correspond to the dates January 1, through December 31, You might see an error message at the end of the import operation about data that was deleted or lost during the operation, or when you open the table in Datasheet view, you might see that some field values are blank.
If the source columns in Excel are not formatted, or the first eight source rows contain values of different data types, open the source worksheet and do the following:. Move the rows so that the first eight rows in each column do not contain values of different data types. During the import operation, select the appropriate data type for each field.
If the data type is incorrect, you might see null values or incorrect values in the entire column after the import operation has completed. The preceding steps can help minimize the appearance of null values. The following table lists cases in which you will still see null values:. Replace all text values with values that match the data type of the destination field and then try importing again. You will see seemingly random five-digit numbers instead of the actual date values in the following situations:.
The source column in the worksheet contains only numeric values in the first eight rows, but contains some date values in the subsequent rows. These date values will be converted incorrectly. The source column contains date values in some of the first eight rows, and you attempted to import it into a numeric field. To avoid this, replace the date values with numeric values in the source column and try importing again.
Sometimes, if a column that contains mostly date values also contains several text values, all the date values might appear as seemingly random five-digit numbers. To avoid this, replace the text values with date values and then try importing again. You will see seemingly random date values instead of the actual numeric values in the following situations:. The source column contains only date values in the first eight rows, but contains some numeric values in the subsequent rows.
These numeric values will be converted incorrectly. The source column contains numeric values in some of the first eight rows, and you attempted to import it into a date field.
To avoid this, replace the numeric values with date values in the source column and then try importing again. In addition, you might want to review the error log table mentioned in the last page of the wizard in Datasheet view.
The table has three fields — Error, Field, and Row. Each row contains information about a specific error, and the contents of the Error field should help you troubleshoot the problem. A value in the file is too large for the FieldSize property setting for this field. A value in the worksheet is the wrong data type for this field. The value might be missing or might appear incorrect in the destination field.
See the previous table for more information how to troubleshoot this issue. A value breaks the rule set by using the ValidationRule property for this field or for the table.
A null value isn't allowed in this field because the Required property for the field is set to Yes. The data that you are importing contains a Null value that you attempted to append to an AutoNumber field. A text value contains the text delimiter character usually double quotation marks. Whenever a value contains the delimiter character, the character must be repeated twice in the text file; for example:.
By linking an Access database to data in another program, you can use the querying and reporting tools that Access provides without having to maintain a copy of the Excel data in your database. When you link to an Excel worksheet or a named range, Access creates a new table that is linked to the source cells. Any changes that you make to the source cells in Excel appear in the linked table. However, you cannot edit the contents of the corresponding table in Access.
If you want to add, edit, or delete data, you must make the changes in the source file. You want to continue to keep your data in Excel worksheets, but be able to use the powerful querying and reporting features of Access. Your department or workgroup uses Access, but data from external sources that you work with is in Excel worksheets.
You don't want to maintain copies of external data, but want to be able to work with it in Access. When you link to an Excel file, Access creates a new table, often referred to as a linked table. The table shows the data in the source worksheet or named range, but it doesn't actually store the data in the database. You cannot link Excel data to an existing table in the database.
This means that you cannot append data to an existing table by performing a linking operation. Any changes that you make to the data in Excel are automatically reflected in the linked table. However, the contents and structure of a linked table in Access are read-only. It's easy to throw links up on your page. That's not enough.
We need to make our links accessible to all readers, regardless of their current context and which tools they prefer. For example:. Good link text: Download Firefox. Bad link text: Click here to download Firefox. When linking to a resource that will be downloaded like a PDF or Word document , streamed like video or audio , or has another potentially unexpected effect opens a popup window, or loads a Flash movie , you should add clear wording to reduce any confusion.
When you are linking to a resource that's to be downloaded rather than opened in the browser, you can use the download attribute to provide a default save filename. Here's an example with a download link to the latest Windows version of Firefox:. For this exercise, we'd like you to link some pages together with a navigation menu to create a multi-page website. This is one common way in which a website is created — the same page structure is used on every page, including the same navigation menu, so when links are clicked it gives the impression that you are staying in the same place, and different content is being brought up.
You'll need to make local copies of the following four pages, all in the same directory. For a complete file list, see the navigation-menu-start directory:. Note: If you get stuck, or aren't sure if you have got it right, you can check the navigation-menu-marked-up directory to see the correct answer. It's possible to create links or buttons that, when clicked, open a new outgoing email message rather than linking to a resource or page.
In its most basic and commonly used form, a mailto: link indicates the email address of the intended recipient. This results in a link that looks like this: Send email to nowhere. In fact, the email address is optional. If you omit it and your href is "mailto:", a new outgoing email window will be opened by the user's email client with no destination address.
This is often useful as "Share" links that users can click to send an email to an address of their choosing. In addition to the email address, you can provide other information.
In fact, any standard mail header fields can be added to the mailto URL you provide. The most commonly used of these are "subject", "cc", and "body" which is not a true header field, but allows you to specify a short content message for the new email. Each field and its value is specified as a query term.
Note: The values of each field must be URL-encoded, that is with non-printing characters invisible characters like tabs, carriage returns, and page breaks and spaces percent-escaped. Also, note the use of the question mark? This is standard URL query notation. You've reached the end of this article, but can you remember the most important information? You can find some further tests to verify that you've retained this information before you move on — see Test your skills: Links.
That's it for links, for now anyway! Bias can entail manipulation in the analysis or reporting of findings. Selective or distorted reporting is a typical form of such bias. We may assume that u does not depend on whether a true relationship exists or not. This is not an unreasonable assumption, since typically it is impossible to know which relationships are indeed true. Thus, with increasing bias, the chances that a research finding is true diminish considerably.
This is shown for different levels of power and for different pre-study odds in Figure 1. Conversely, true research findings may occasionally be annulled because of reverse bias.
There is no good large-scale empirical evidence on how frequently such reverse bias may occur across diverse research fields. However, it is probably fair to say that reverse bias is not as common. Moreover measurement errors and inefficient use of data are probably becoming less frequent problems, since measurement error has decreased with technological advances in the molecular era and investigators are becoming increasingly sophisticated about their data.
Regardless, reverse bias may be modeled in the same way as bias above. Also reverse bias should not be confused with chance variability that may lead to missing a true relationship because of chance.
Panels correspond to power of 0. Several independent teams may be addressing the same sets of research questions. As research efforts are globalized, it is practically the rule that several research teams, often dozens of them, may probe the same or similar questions. Unfortunately, in some areas, the prevailing mentality until now has been to focus on isolated discoveries by single teams and interpret research experiments in isolation.
An increasing number of questions have at least one study claiming a research finding, and this receives unilateral attention. The probability that at least one study, among several done on the same question, claims a statistically significant research finding is easy to estimate.
This is shown for different levels of power and for different pre-study odds in Figure 2. A practical example is shown in Box 1. Based on the above considerations, one may deduce several interesting corollaries about the probability that a research finding is indeed true.
Let us assume that a team of investigators performs a whole genome association study to test whether any of , gene polymorphisms are associated with susceptibility to schizophrenia. Based on what we know about the extent of heritability of the disease, it is reasonable to expect that probably around ten gene polymorphisms among those tested would be truly associated with schizophrenia, with relatively similar odds ratios around 1.
Then it can be estimated that if a statistically significant association is found with the p -value barely crossing the 0. Such manipulation could be done, for example, with serendipitous inclusion or exclusion of certain patients or controls, post hoc subgroup analyses, investigation of genetic contrasts that were not originally specified, changes in the disease or control definitions, and various combinations of selective or distorted reporting of the results.
Furthermore, even in the absence of any bias, when ten independent research teams perform similar experiments around the world, if one of them finds a formally statistically significant association, the probability that the research finding is true is only 1.
Corollary 1: The smaller the studies conducted in a scientific field, the less likely the research findings are to be true. Thus, other factors being equal, research findings are more likely true in scientific fields that undertake large studies, such as randomized controlled trials in cardiology several thousand subjects randomized [ 14 ] than in scientific fields with small studies, such as most research of molecular predictors sample sizes fold smaller [ 15 ].
Corollary 2: The smaller the effect sizes in a scientific field, the less likely the research findings are to be true. Power is also related to the effect size. Thus research findings are more likely true in scientific fields with large effects, such as the impact of smoking on cancer or cardiovascular disease relative risks 3—20 , than in scientific fields where postulated effects are small, such as genetic risk factors for multigenetic diseases relative risks 1. Modern epidemiology is increasingly obliged to target smaller effect sizes [ 16 ].
Consequently, the proportion of true research findings is expected to decrease. In the same line of thinking, if the true effect sizes are very small in a scientific field, this field is likely to be plagued by almost ubiquitous false positive claims. For example, if the majority of true genetic or nutritional determinants of complex diseases confer relative risks less than 1. Corollary 3: The greater the number and the lesser the selection of tested relationships in a scientific field, the less likely the research findings are to be true.
As shown above, the post-study probability that a finding is true PPV depends a lot on the pre-study odds R. Thus, research findings are more likely true in confirmatory designs, such as large phase III randomized controlled trials, or meta-analyses thereof, than in hypothesis-generating experiments. Fields considered highly informative and creative given the wealth of the assembled and tested information, such as microarrays and other high-throughput discovery-oriented research [ 4 , 8 , 17 ], should have extremely low PPV.
Corollary 4: The greater the flexibility in designs, definitions, outcomes, and analytical modes in a scientific field, the less likely the research findings are to be true. For several research designs, e. Adherence to common standards is likely to increase the proportion of true findings. The same applies to outcomes. True findings may be more common when outcomes are unequivocal and universally agreed e.
Similarly, fields that use commonly agreed, stereotyped analytical methods e. Regardless, even in the most stringent research designs, bias seems to be a major problem. For example, there is strong evidence that selective outcome reporting, with manipulation of the outcomes and analyses reported, is a common problem even for randomized trails [ 25 ].
Simply abolishing selective publication would not make this problem go away. Corollary 5: The greater the financial and other interests and prejudices in a scientific field, the less likely the research findings are to be true. Conflicts of interest and prejudice may increase bias, u. Conflicts of interest are very common in biomedical research [ 26 ], and typically they are inadequately and sparsely reported [ 26 , 27 ].
Prejudice may not necessarily have financial roots. Scientists in a given field may be prejudiced purely because of their belief in a scientific theory or commitment to their own findings. Many otherwise seemingly independent, university-based studies may be conducted for no other reason than to give physicians and researchers qualifications for promotion or tenure. Such nonfinancial conflicts may also lead to distorted reported results and interpretations.
Prestigious investigators may suppress via the peer review process the appearance and dissemination of findings that refute their findings, thus condemning their field to perpetuate false dogma. Empirical evidence on expert opinion shows that it is extremely unreliable [ 28 ].
Corollary 6: The hotter a scientific field with more scientific teams involved , the less likely the research findings are to be true. This seemingly paradoxical corollary follows because, as stated above, the PPV of isolated findings decreases when many teams of investigators are involved in the same field. This may explain why we occasionally see major excitement followed rapidly by severe disappointments in fields that draw wide attention.
With many teams working on the same field and with massive experimental data being produced, timing is of the essence in beating competition. In that case, it may be attractive to refute a claim made in some prestigious journal. The term Proteus phenomenon has been coined to describe this phenomenon of rapidly alternating extreme research claims and extremely opposite refutations [ 29 ]. Empirical evidence suggests that this sequence of extreme opposites is very common in molecular genetics [ 29 ].
These corollaries consider each factor separately, but these factors often influence each other. For example, investigators working in fields where true effect sizes are perceived to be small may be more likely to perform large studies than investigators working in fields where true effect sizes are perceived to be large. Or prejudice may prevail in a hot scientific field, further undermining the predictive value of its research findings.
Highly prejudiced stakeholders may even create a barrier that aborts efforts at obtaining and disseminating opposing results. Conversely, the fact that a field is hot or has strong invested interests may sometimes promote larger studies and improved standards of research, enhancing the predictive value of its research findings.
Or massive discovery-oriented testing may result in such a large yield of significant relationships that investigators have enough to report and search further and thus refrain from data dredging and manipulation.
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